Installed R Statistical Software Packages (Ubuntu 22.04)
This table lists all R pre-installed packages that are immediately available in every CoCalc project running on the default "Ubuntu 22.04" image, along with their version numbers. If something is missing, you can install it yourself, or request that we install them.
Learn more about R functionality in CoCalc.
Available Environments
- R Project:
The "official" R distribution from the R Project, installed system-wide.
R version 4.4.1 (2024-06-14) -- "Race for Your Life" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under the terms of the GNU General Public License versions 2 or 3. For more information about these matters see https://www.gnu.org/licenses/.- SageMath's R:
R distribution within SageMath. Start via
R-sage
or select the appropriate kernel.R version 4.4.1 (2024-06-14) -- "Race for Your Life" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under the terms of the GNU General Public License versions 2 or 3. For more information about these matters see https://www.gnu.org/licenses/.
Showing 5294 libraries
Library | R Project | SageMath's R |
---|---|---|
abbyyR Access to Abbyy Optical Character Recognition (OCR) API | 0.5.5 | 0.5.5 |
abc Tools for Approximate Bayesian Computation (ABC) | 2.2.1 | 2.2.1 |
abc.data Data Only: Tools for Approximate Bayesian Computation (ABC) | 1.0 | 1.0 |
ABCanalysis Computed ABC Analysis | 1.2.1 | 1.2.1 |
ABCoptim Implementation of Artificial Bee Colony (ABC) Optimization | 0.15.0 | 0.15.0 |
abcrf Approximate Bayesian Computation via Random Forests | 1.9 | 1.9 |
abess Fast Best Subset Selection | 0.4.8 | 0.4.8 |
abglasso Adaptive Bayesian Graphical Lasso | 0.1.1 | 0.1.1 |
abind Combine Multidimensional Arrays | 1.4-5 | 1.4-5 |
abtest Bayesian A/B Testing | 1.0.1 | 1.0.1 |
acc Exploring Accelerometer Data | 1.3.3 | 1.3.3 |
accelerometry Functions for Processing Accelerometer Data | 3.1.2 | 3.1.2 |
accelmissing Missing Value Imputation for Accelerometer Data | 1.4 | 1.4 |
acebayes Optimal Bayesian Experimental Design using the ACE Algorithm | 1.10 | 1.10 |
acepack ACE and AVAS for Selecting Multiple Regression Transformations | 1.4.2 | 1.4.2 |
acp Autoregressive Conditional Poisson | 2.1 | 2.1 |
acs Download, Manipulate, and Present American Community Survey and Decennial Data from the US Census | 2.1.4 | 2.1.4 |
ACSWR A Companion Package for the Book "A Course in Statistics with R" | 1.0 | 1.0 |
ActCR Extract Circadian Rhythms Metrics from Actigraphy Data | 0.3.0 | 0.3.0 |
activityCounts Generate ActiLife Counts | 0.1.2 | 0.1.2 |
actuar Actuarial Functions and Heavy Tailed Distributions | 3.3-4 | 3.3-4 |
ada The R Package Ada for Stochastic Boosting | 2.0-5 | 2.0-5 |
adabag Applies Multiclass AdaBoost.M1, SAMME and Bagging | 5.0 | 5.0 |
adagio Discrete and Global Optimization Routines | 0.9.2 | 0.9.2 |
adaptivetau Tau-Leaping Stochastic Simulation | 2.3 | 2.3 |
adaptMCMC Implementation of a Generic Adaptive Monte Carlo Markov Chain Sampler | 1.5 | 1.5 |
adaptMT Adaptive P-Value Thresholding for Multiple Hypothesis Testing with Side Information | 1.0.0 | 1.0.0 |
adaptTest Adaptive Two-Stage Tests | 1.2 | 1.2 |
AdaSampling Adaptive Sampling for Positive Unlabeled and Label Noise Learning | 1.3 | 1.3 |
addhazard Fit Additive Hazards Models for Survival Analysis | 1.1.0 | 1.1.0 |
additivityTests Additivity Tests in the Two Way Anova with Single Sub-Class Numbers | 1.1-4.1 | 1.1-4.1 |
ade4 Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences | 1.7-22 | 1.7-22 |
adegenet Exploratory Analysis of Genetic and Genomic Data | 2.1.10 | 2.1.10 |
adegraphics An S4 Lattice-Based Package for the Representation of Multivariate Data | 1.0-21 | 1.0-21 |
adehabitatHR Home Range Estimation | 0.4.21 | 0.4.21 |
adehabitatHS Analysis of Habitat Selection by Animals | 0.3.17 | 0.3.17 |
adehabitatLT Analysis of Animal Movements | 0.3.27 | 0.3.27 |
adehabitatMA Tools to Deal with Raster Maps | 0.3.16 | 0.3.16 |
adephylo Exploratory Analyses for the Phylogenetic Comparative Method | 1.1-16 | 1.1-16 |
AdequacyModel Adequacy of Probabilistic Models and General Purpose Optimization | 2.0.0 | 2.0.0 |
adespatial Multivariate Multiscale Spatial Analysis | 0.3-16 | 0.3-16 |
ADGofTest Anderson-Darling GoF test | 0.3 | 0.3 |
adimpro Adaptive Smoothing of Digital Images | 0.9.6 | 0.9.6 |
adiv Analysis of Diversity | 2.1.2 | 2.1.2 |
admisc Adrian Dusa's Miscellaneous | 0.34 | 0.34 |
AdMit Adaptive Mixture of Student-t Distributions | 2.1.9 | 2.1.9 |
ADPclust Fast Clustering Using Adaptive Density Peak Detection | 0.7 | 0.7 |
ADPF Use Least Squares Polynomial Regression and Statistical Testing to Improve Savitzky-Golay | 0.0.1 | 0.0.1 |
ads Spatial Point Patterns Analysis | 1.5-10 | 1.5-10 |
AdvancedBasketballStats Advanced Basketball Statistics | 1.0.1 | 1.0.1 |
AER Applied Econometrics with R | 1.2-10 | 1.2-10 |
AeRobiology A Computational Tool for Aerobiological Data | 2.0.1 | 2.0.1 |
afex Analysis of Factorial Experiments | 1.3-0 | 1.3-0 |
affxparser | 1.70.0 | 1.70.0 |
affy | 1.82.0 | 1.82.0 |
affydata | 1.46.0 | 1.46.0 |
affyio | 1.74.0 | 1.74.0 |
affyPLM | 1.74.1 | 1.74.1 |
aftgee Accelerated Failure Time Model with Generalized Estimating Equations | 1.2.0 | 1.2.0 |
aggregation p-Value Aggregation Methods | 1.0.1 | 1.0.1 |
agricolae Statistical Procedures for Agricultural Research | 1.3-5 | 1.3-5 |
agridat Agricultural Datasets | 1.22 | 1.22 |
AGSDest Estimation in Adaptive Group Sequential Trials | 2.3.4 | 2.3.4 |
ahaz Regularization for Semiparametric Additive Hazards Regression | 1.15 | 1.15 |
aion Archaeological Time Series | 1.1.0 | 1.1.0 |
AIPW Augmented Inverse Probability Weighting | 0.6.3.2 | 0.6.3.2 |
airGR Suite of GR Hydrological Models for Precipitation-Runoff Modelling | 1.7.6 | 1.7.6 |
airGRdatassim Ensemble-Based Data Assimilation with GR Hydrological Models | 0.1.3 | 0.1.3 |
airGRteaching Teaching Hydrological Modelling with the GR Rainfall-Runoff Models ('Shiny' Interface Included) | 0.3.2 | 0.3.2 |
airports Data on Airports | 0.1.0 | 0.1.0 |
airr AIRR Data Representation Reference Library | 1.5.0 | 1.5.0 |
ajv Another JSON Schema Validator | 1.0.0 | 1.0.0 |
akima Interpolation of Irregularly and Regularly Spaced Data | 0.6-3.4 | 0.6-3.4 |
alabama Constrained Nonlinear Optimization | 2023.1.0 | 2023.1.0 |
ald The Asymmetric Laplace Distribution | 1.3.1 | 1.3.1 |
AlgDesign Algorithmic Experimental Design | 1.2.1 | 1.2.1 |
alleHap Allele Imputation and Haplotype Reconstruction from Pedigree Databases | 0.9.9 | 0.9.9 |
allestimates Effect Estimates from All Models | 0.2.3 | 0.2.3 |
alluvial Alluvial Diagrams | 0.1-2 | 0.1-2 |
alpaca Fit GLM's with High-Dimensional k-Way Fixed Effects | 0.3.4 | 0.3.4 |
alphahull Generalization of the Convex Hull of a Sample of Points in the Plane | 2.5 | 2.5 |
alphavantager Lightweight Interface to the Alpha Vantage API | 0.1.3 | 0.1.3 |
altmeta Alternative Meta-Analysis Methods | 4.1 | 4.1 |
ALTopt Optimal Experimental Designs for Accelerated Life Testing | 0.1.2 | 0.1.2 |
amanida Meta-Analysis for Non-Integral Data | 0.2.3 | 0.2.3 |
amap Another Multidimensional Analysis Package | 0.8-19 | 0.8-19 |
Amelia A Program for Missing Data | 1.8.1 | 1.8.1 |
AmericanCallOpt This package includes pricing function for selected American<U+000a>call options with underlying assets that generate payouts. | 0.95 | 0.95 |
ammiBayes Bayesian Ammi Model for Continuous Data | 1.0-1 | 1.0-1 |
AMORE Artificial Neural Network Training and Simulating | 0.2-16 | 0.2-16 |
amt Animal Movement Tools | 0.2.1.0 | 0.2.1.0 |
AnaCoDa Analysis of Codon Data under Stationarity using a Bayesian Framework | 0.1.4.4 | 0.1.4.4 |
anacor Simple and Canonical Correspondence Analysis | 1.1-4 | 1.1-4 |
analogsea Interface to 'DigitalOcean' | 1.0.7.2 | 1.0.7.2 |
analogue Analogue and Weighted Averaging Methods for Palaeoecology | 0.17-6 | 0.17-6 |
Andromeda Asynchronous Disk-Based Representation of Massive Data | 0.6.5 | 0.6.5 |
anesrake ANES Raking Implementation | 0.80 | 0.80 |
animalTrack Animal track reconstruction for high frequency 2-dimensional<U+000a>(2D) or 3-dimensional (3D) movement data. | 1.0.0 | 1.0.0 |
animation A Gallery of Animations in Statistics and Utilities to Create Animations | 2.7 | 2.7 |
anipaths Animation of Multiple Trajectories with Uncertainty | 0.10.1 | 0.10.1 |
anMC Compute High Dimensional Orthant Probabilities | 0.2.5 | 0.2.5 |
annotate | 1.82.0 | 1.82.0 |
AnnotationDbi | 1.66.0 | 1.66.0 |
AnnotationFilter | 1.22.0 | 1.22.0 |
AnnotationHub | 3.12.0 | 3.12.0 |
anomaly Detecting Anomalies in Data | 4.0.2 | 4.0.2 |
Anthropometry Statistical Methods for Anthropometric Data | 1.19 | 1.19 |
antitrust Tools for Antitrust Practitioners | 0.99.25 | 0.99.25 |
anyLib Install and Load Any Package from CRAN, Bioconductor or Github | 1.0.5 | 1.0.5 |
anytime Anything to 'POSIXct' or 'Date' Converter | 0.3.9 | 0.3.9 |
aod Analysis of Overdispersed Data | 1.3.3 | 1.3.3 |
aoos Another Object Orientation System | 0.5.0 | 0.5.0 |
AovBay Classic, Nonparametric and Bayesian One-Way Analysis of Variance Panel | 0.1.0 | 0.1.0 |
apcluster Affinity Propagation Clustering | 1.4.11 | 1.4.11 |
ape Analyses of Phylogenetics and Evolution | 5.7-1 | 5.7-1 |
apex Phylogenetic Methods for Multiple Gene Data | 1.0.4 | 1.0.4 |
APFr Multiple Testing Approach using Average Power Function (APF) and Bayes FDR Robust Estimation | 1.0.2 | 1.0.2 |
aplore3 Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic Regression" (3rd Ed., 2013) | 0.9 | 0.9 |
aplot Decorate a 'ggplot' with Associated Information | 0.2.3 | 0.2.3 |
aplpack Another Plot Package: 'Bagplots', 'Iconplots', 'Summaryplots', Slider Functions and Others | 1.3.5 | 1.3.5 |
apollo Tools for Choice Model Estimation and Application | 0.3.1 | 0.3.1 |
AppliedPredictiveModeling Functions and Data Sets for 'Applied Predictive Modeling' | 1.1-7 | 1.1-7 |
approximator Bayesian Prediction of Complex Computer Codes | 1.2-8 | 1.2-8 |
approxmatch Approximately Optimal Fine Balance Matching with Multiple Groups | 2.0 | 2.0 |
aprof Amdahl's Profiler, Directed Optimization Made Easy | 0.4.1 | 0.4.1 |
apt Asymmetric Price Transmission | 3.0 | 3.0 |
APtools Average Positive Predictive Values (AP) for Binary Outcomes and Censored Event Times | 6.8.8 | 6.8.8 |
aqp Algorithms for Quantitative Pedology | 2.0.2 | 2.0.2 |
AquaEnv Integrated Development Toolbox for Aquatic Chemical Model Generation | 1.0-4 | 1.0-4 |
ARCensReg Fitting Univariate Censored Linear Regression Model with Autoregressive Errors | 3.0.1 | 3.0.1 |
ArchaeoChron Bayesian Modeling of Archaeological Chronologies | 0.1 | 0.1 |
ArchaeoPhases Post-Processing of Markov Chain Monte Carlo Simulations for Chronological Modelling | 1.8 | 1.8 |
archetypes Archetypal Analysis | 2.2-0.1 | 2.2-0.1 |
archive Multi-Format Archive and Compression Support | 1.1.9 | 1.1.9 |
archivist Tools for Storing, Restoring and Searching for R Objects | 2.3.6 | 2.3.6 |
ArDec Time Series Autoregressive-Based Decomposition | 2.1-1 | 2.1-1 |
areal Areal Weighted Interpolation | 0.1.8 | 0.1.8 |
argo Accurate Estimation of Influenza Epidemics using Google Search Data | 3.0.2 | 3.0.2 |
argosfilter Argos Locations Filter | 0.70 | 0.70 |
argparse Command Line Optional and Positional Argument Parser | 2.1.3 | 2.1.3 |
argparser Command-Line Argument Parser | 0.7.1 | 0.7.1 |
aricode Efficient Computations of Standard Clustering Comparison Measures | 1.0.3 | 1.0.3 |
arkhe Tools for Cleaning Rectangular Data | 1.7.0 | 1.7.0 |
arm Data Analysis Using Regression and Multilevel/Hierarchical Models | 1.13-1 | 1.13-1 |
AROC Covariate-Adjusted Receiver Operating Characteristic Curve Inference | 1.0-4 | 1.0-4 |
arpr Advanced R Pipes | 0.1.2 | 0.1.2 |
arrangements Fast Generators and Iterators for Permutations, Combinations, Integer Partitions and Compositions | 1.1.9 | 1.1.9 |
arrow Integration to 'Apache' 'Arrow' | 10.0.1 | 10.0.1 |
ars Adaptive Rejection Sampling | 0.6 | 0.6 |
arsenal An Arsenal of 'R' Functions for Large-Scale Statistical Summaries | 3.6.3 | 3.6.3 |
arules Mining Association Rules and Frequent Itemsets | 1.7-7 | 1.7-7 |
arulesCBA Classification Based on Association Rules | 1.2.5 | 1.2.5 |
arulesSequences Mining Frequent Sequences | 0.2-30 | 0.2-30 |
aRxiv Interface to the arXiv API | 0.10 | 0.10 |
asaur Data Sets for "Applied Survival Analysis Using R"" | 0.50 | 0.50 |
asbio A Collection of Statistical Tools for Biologists | 1.9-7 | 1.9-7 |
ascii Export R Objects to Several Markup Languages | 2.4 | 2.4 |
asd Simulations for Adaptive Seamless Designs | 2.2 | 2.2 |
ash David Scott's ASH Routines | 1.0-15 | 1.0-15 |
ashr Methods for Adaptive Shrinkage, using Empirical Bayes | 2.2-63 | 2.2-63 |
AsioHeaders 'Asio' C++ Header Files | 1.22.1-2 | 1.22.1-2 |
askpass Password Entry Utilities for R, Git, and SSH | 1.2.0 | 1.2.0 |
ASPBay Bayesian Inference on Causal Genetic Variants using Affected Sib-Pairs Data | 1.2 | 1.2 |
aspect A General Framework for Multivariate Analysis with Optimal Scaling | 1.0-6 | 1.0-6 |
ASSA Applied Singular Spectrum Analysis (ASSA) | 2.0 | 2.0 |
assemblerr Assembly of Pharmacometric Models | 0.1.1 | 0.1.1 |
assertive Readable Check Functions to Ensure Code Integrity | 0.3-6 | 0.3-6 |
assertive.base A Lightweight Core of the 'assertive' Package | 0.0-9 | 0.0-9 |
assertive.code Assertions to Check Properties of Code | 0.0-4 | 0.0-4 |
assertive.data Assertions to Check Properties of Data | 0.0-3 | 0.0-3 |
assertive.data.uk Assertions to Check Properties of Strings | 0.0-2 | 0.0-2 |
assertive.data.us Assertions to Check Properties of Strings | 0.0-2 | 0.0-2 |
assertive.datetimes Assertions to Check Properties of Dates and Times | 0.0-3 | 0.0-3 |
assertive.files Assertions to Check Properties of Files | 0.0-2 | 0.0-2 |
assertive.matrices Assertions to Check Properties of Matrices | 0.0-2 | 0.0-2 |
assertive.models Assertions to Check Properties of Models | 0.0-2 | 0.0-2 |
assertive.numbers Assertions to Check Properties of Numbers | 0.0-2 | 0.0-2 |
assertive.properties Assertions to Check Properties of Variables | 0.0-5 | 0.0-5 |
assertive.reflection Assertions for Checking the State of R | 0.0-5 | 0.0-5 |
assertive.sets Assertions to Check Properties of Sets | 0.0-3 | 0.0-3 |
assertive.strings Assertions to Check Properties of Strings | 0.0-3 | 0.0-3 |
assertive.types Assertions to Check Types of Variables | 0.0-3 | 0.0-3 |
assertthat Easy Pre and Post Assertions | 0.2.1 | 0.2.1 |
AssetCorr Estimating Asset Correlations from Default Data | 1.0.4 | 1.0.4 |
aster Aster Models | 1.1-3 | 1.1-3 |
aster2 Aster Models | 0.3 | 0.3 |
astrodatR Astronomical Data | 0.1 | 0.1 |
astroFns Astronomy: Time and Position Functions, Misc. Utilities | 4.2-1 | 4.2-1 |
astrolibR Astronomy Users Library | 0.1 | 0.1 |
astsa Applied Statistical Time Series Analysis | 2.1 | 2.1 |
asymmetry Multidimensional Scaling of Asymmetric Proximities | 2.0.4 | 2.0.4 |
asypow Calculate Power Utilizing Asymptotic Likelihood Ratio Methods | 2015.6.25 | 2015.6.25 |
ata Automated Test Assembly | 1.1.1 | 1.1.1 |
ath1121501.db | 3.13.0 | 3.13.0 |
ath1121501cdf | 2.18.0 | 2.18.0 |
atmcmc Automatically Tuned Markov Chain Monte Carlo | 1.0 | 1.0 |
ATmet Advanced Tools for Metrology | 1.2.1 | 1.2.1 |
aTSA Alternative Time Series Analysis | 3.1.2 | 3.1.2 |
attempt Tools for Defensive Programming | 0.3.1 | 0.3.1 |
attention Self-Attention Algorithm | 0.4.0 | 0.4.0 |
audio Audio Interface for R | 0.1-11 | 0.1-11 |
autoFRK Automatic Fixed Rank Kriging | 1.4.3 | 1.4.3 |
autohd High Dimensional Bayesian Survival Mediation Analysis | 0.1.0 | 0.1.0 |
autoimage Multiple Heat Maps for Projected Coordinates | 2.2.3 | 2.2.3 |
automap Automatic Interpolation Package | 1.1-12 | 1.1-12 |
autostsm Automatic Structural Time Series Models | 3.1.2 | 3.1.2 |
av Working with Audio and Video in R | 0.8.3 | 0.8.3 |
aweek Convert Dates to Arbitrary Week Definitions | 1.0.3 | 1.0.3 |
aws Adaptive Weights Smoothing | 2.5-3 | 2.5-3 |
aws.signature Amazon Web Services Request Signatures | 0.6.0 | 0.6.0 |
awsMethods Class and Methods Definitions for Packages 'aws', 'adimpro', 'fmri', 'dwi' | 1.1-1 | 1.1-1 |
AzureAuth Authentication Services for Azure Active Directory | 1.3.3 | 1.3.3 |
AzureCognitive Interface to Azure Cognitive Services | 1.0.1 | 1.0.1 |
AzureContainers Interface to 'Container Instances', 'Docker Registry' and 'Kubernetes' in 'Azure' | 1.3.2 | 1.3.2 |
AzureCosmosR Interface to the 'Azure Cosmos DB' 'NoSQL' Database Service | 1.0.0 | 1.0.0 |
AzureGraph Simple Interface to 'Microsoft Graph' | 1.3.4 | 1.3.4 |
AzureKusto Interface to 'Kusto'/'Azure Data Explorer' | 1.1.3 | 1.1.3 |
AzureQstor Interface to 'Azure Queue Storage' | 1.0.1 | 1.0.1 |
AzureRMR Interface to 'Azure Resource Manager' | 2.4.4 | 2.4.4 |
AzureStor Storage Management in 'Azure' | 3.7.0 | 3.7.0 |
AzureTableStor Interface to the Table Storage Service in 'Azure' | 1.0.0 | 1.0.0 |
AzureVision Interface to Azure Computer Vision Services | 1.0.2 | 1.0.2 |
AzureVM Virtual Machines in 'Azure' | 2.2.2 | 2.2.2 |
babar Bayesian Bacterial Growth Curve Analysis in R | 1.0 | 1.0 |
BaBooN Bayesian Bootstrap Predictive Mean Matching - Multiple and Single Imputation for Discrete Data | 0.2-0 | 0.2-0 |
BACCO Bayesian Analysis of Computer Code Output (BACCO) | 2.1-0 | 2.1-0 |
BACCT Bayesian Augmented Control for Clinical Trials | 1.0 | 1.0 |
backbone Extracts the Backbone from Graphs | 2.1.3 | 2.1.3 |
backports Reimplementations of Functions Introduced Since R-3.0.0 | 1.4.1 | 1.4.1 |
backtest Exploring Portfolio-Based Conjectures About Financial Instruments | 0.3-4 | 0.3-4 |
bacondecomp Goodman-Bacon Decomposition | 0.1.1 | 0.1.1 |
baggr Bayesian Aggregate Treatment Effects | 0.7.6 | 0.7.6 |
bain Bayes Factors for Informative Hypotheses | 0.2.10 | 0.2.10 |
BalancedSampling Balanced and Spatially Balanced Sampling | 1.6.3 | 1.6.3 |
BaM Functions and Datasets for "Bayesian Methods: A Social and Behavioral Sciences Approach" | 1.0.2 | 1.0.2 |
bama High Dimensional Bayesian Mediation Analysis | 1.3.0 | 1.3.0 |
bamdit Bayesian Meta-Analysis of Diagnostic Test Data | 3.4.0 | 3.4.0 |
bamlss Bayesian Additive Models for Location, Scale, and Shape (and Beyond) | 1.2-2 | 1.2-2 |
BAMMtools Analysis and Visualization of Macroevolutionary Dynamics on Phylogenetic Trees | 2.1.11 | 2.1.11 |
bang Bayesian Analysis, No Gibbs | 1.0.3 | 1.0.3 |
BANOVA Hierarchical Bayesian ANOVA Models | 1.2.1 | 1.2.1 |
BaPreStoPro Bayesian Prediction of Stochastic Processes | 0.1 | 0.1 |
BART Bayesian Additive Regression Trees | 2.9.6 | 2.9.6 |
bartBMA Bayesian Additive Regression Trees using Bayesian Model Averaging | 1.0 | 1.0 |
bartCause Causal Inference using Bayesian Additive Regression Trees | 1.0-6 | 1.0-6 |
bartMachine Bayesian Additive Regression Trees | 1.3.4.1 | 1.3.4.1 |
bartMachineJARs bartMachine JARs | 1.2.1 | 1.2.1 |
BAS Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling | 1.7.1 | 1.7.1 |
basad Bayesian Variable Selection with Shrinking and Diffusing Priors | 0.3.0 | 0.3.0 |
base | 4.4.1 | 4.4.1 |
base64 Base64 Encoder and Decoder | 2.0.1 | 2.0.1 |
base64enc Tools for base64 encoding | 0.1-3 | 0.1-3 |
base64url Fast and URL-Safe Base64 Encoder and Decoder | 1.4 | 1.4 |
baseballDBR Sabermetrics and Advanced Baseball Statistics | 0.1.2 | 0.1.2 |
baseballr Acquiring and Analyzing Baseball Data | 1.6.0 | 1.6.0 |
basefun Infrastructure for Computing with Basis Functions | 1.1-4 | 1.1-4 |
baseline Baseline Correction of Spectra | 1.3-5 | 1.3-5 |
basicMCMCplots Trace Plots, Density Plots and Chain Comparisons for MCMC Samples | 0.2.7 | 0.2.7 |
BaSkePro Bayesian Model to Archaeological Faunal Skeletal Profiles | 1.1.1 | 1.1.1 |
BasketballAnalyzeR Analysis and Visualization of Basketball Data | 0.5.0 | 0.5.0 |
BASS Bayesian Adaptive Spline Surfaces | 1.3.1 | 1.3.1 |
BaSTA Age-Specific Bayesian Survival Trajectory Analysis from Incomplete Census or Capture-Recapture/Recovery Data | 1.9.5 | 1.9.5 |
batch Batching Routines in Parallel and Passing Command-Line Arguments to R | 1.1-5 | 1.1-5 |
BatchExperiments Statistical Experiments on Batch Computing Clusters | 1.4.3 | 1.4.3 |
BatchJobs Batch Computing with R | 1.9 | 1.9 |
batchmeans Consistent Batch Means Estimation of Monte Carlo Standard Errors | 1.0-4 | 1.0-4 |
batchtools Tools for Computation on Batch Systems | 0.9.17 | 0.9.17 |
BAwiR Analysis of Basketball Data | 1.3.2 | 1.3.2 |
baycn Bayesian Inference for Causal Networks | 1.2.0 | 1.2.0 |
bayefdr Bayesian Estimation and Optimisation of Expected False Discovery Rate | 0.2.1 | 0.2.1 |
bayes4psy User Friendly Bayesian Data Analysis for Psychology | 1.2.12 | 1.2.12 |
bayesAB Fast Bayesian Methods for AB Testing | 1.1.3 | 1.1.3 |
bayesammi Bayesian Estimation of the Additive Main Effects and Multiplicative Interaction Model | 0.1.0 | 0.1.0 |
bayesanova Bayesian Inference in the Analysis of Variance via Markov Chain Monte Carlo in Gaussian Mixture Models | 1.5 | 1.5 |
BayesARIMAX Bayesian Estimation of ARIMAX Model | 0.1.1 | 0.1.1 |
BayesBinMix Bayesian Estimation of Mixtures of Multivariate Bernoulli Distributions | 1.4.1 | 1.4.1 |
bayesbio Miscellaneous Functions for Bioinformatics and Bayesian Statistics | 1.0.0 | 1.0.0 |
bayesboot An Implementation of Rubin's (1981) Bayesian Bootstrap | 0.2.2 | 0.2.2 |
BayesBP Bayesian Estimation using Bernstein Polynomial Fits Rate Matrix | 1.1 | 1.1 |
bayesbr Beta Regression on a Bayesian Model | 0.0.1.0 | 0.0.1.0 |
BayesCACE Bayesian Model for CACE Analysis | 1.2.3 | 1.2.3 |
BayesCombo Bayesian Evidence Combination | 1.0 | 1.0 |
BayesComm Bayesian Community Ecology Analysis | 0.1-2 | 0.1-2 |
bayescopulareg Bayesian Copula Regression | 0.1.3 | 0.1.3 |
bayescount Power Calculations and Bayesian Analysis of Count Distributions and FECRT Data using MCMC | 0.9.99-9 | 0.9.99-9 |
BayesCR Bayesian Analysis of Censored Regression Models Under Scale Mixture of Skew Normal Distributions | 2.1 | 2.1 |
bayesCT Simulation and Analysis of Adaptive Bayesian Clinical Trials | 0.99.3 | 0.99.3 |
BayesCTDesign Two Arm Bayesian Clinical Trial Design with and Without Historical Control Data | 0.6.1 | 0.6.1 |
BayesDA Functions and Datasets for the book "Bayesian Data Analysis" | 2012.04-1 | 2012.04-1 |
bayesDccGarch Methods and Tools for Bayesian Dynamic Conditional Correlation GARCH(1,1) Model | 3.0.4 | 3.0.4 |
bayesdfa Bayesian Dynamic Factor Analysis (DFA) with 'Stan' | 1.3.2 | 1.3.2 |
bayesdistreg Bayesian Distribution Regression | 0.1.0 | 0.1.0 |
bayesDP Implementation of the Bayesian Discount Prior Approach for Clinical Trials | 1.3.6 | 1.3.6 |
BayesFactor Computation of Bayes Factors for Common Designs | 0.9.12-4.7 | 0.9.12-4.7 |
BayesFM Bayesian Inference for Factor Modeling | 0.1.5 | 0.1.5 |
bayesforecast Bayesian Time Series Modeling with Stan | 1.0.1 | 1.0.1 |
bayesGAM Fit Multivariate Response Generalized Additive Models using Hamiltonian Monte Carlo | 0.0.2 | 0.0.2 |
bayesGARCH Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations | 2.1.10 | 2.1.10 |
BayesGESM Bayesian Analysis of Generalized Elliptical Semi-Parametric Models and Flexible Measurement Error Models | 1.4 | 1.4 |
BayesGOF Bayesian Modeling via Frequentist Goodness-of-Fit | 5.2 | 5.2 |
BayesGPfit Fast Bayesian Gaussian Process Regression Fitting | 1.1.0 | 1.1.0 |
BayesGWQS Bayesian Grouped Weighted Quantile Sum Regression | 0.1.1 | 0.1.1 |
bayesian Bindings for Bayesian TidyModels | 0.0.9 | 0.0.9 |
BayesianAnimalTracker Bayesian Melding of GPS and DR Path for Animal Tracking | 1.2 | 1.2 |
bayesianETAS Bayesian Estimation of the ETAS Model for Earthquake Occurrences | 1.0.3 | 1.0.3 |
BayesianFROC FROC Analysis by Bayesian Approaches | 1.0.0 | 1.0.0 |
Bayesiangammareg Bayesian Gamma Regression: Joint Mean and Shape Modeling | 0.1.0 | 0.1.0 |
BayesianGLasso Bayesian Graphical Lasso | 0.2.0 | 0.2.0 |
BayesianLaterality Predict Brain Asymmetry Based on Handedness and Dichotic Listening | 0.1.2 | 0.1.2 |
BayesianTools General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics | 0.1.8 | 0.1.8 |
bayesImageS Bayesian Methods for Image Segmentation using a Potts Model | 0.6-1 | 0.6-1 |
BayesLCA Bayesian Latent Class Analysis | 1.9 | 1.9 |
bayesLife Bayesian Projection of Life Expectancy | 5.2-0 | 5.2-0 |
bayeslincom Linear Combinations of Bayesian Posterior Samples | 1.3.0 | 1.3.0 |
BayesLN Bayesian Inference for Log-Normal Data | 0.2.10 | 0.2.10 |
BayesLogit PolyaGamma Sampling | 2.1 | 2.1 |
bayesloglin Bayesian Analysis of Contingency Table Data | 1.0.1 | 1.0.1 |
bayeslongitudinal Adjust Longitudinal Regression Models Using Bayesian Methodology | 0.1.0 | 0.1.0 |
bayesm Bayesian Inference for Marketing/Micro-Econometrics | 3.1-6 | 3.1-6 |
BayesMallows Bayesian Preference Learning with the Mallows Rank Model | 2.0.1 | 2.0.1 |
BayesMassBal Bayesian Data Reconciliation of Separation Processes | 1.1.0 | 1.1.0 |
bayesmeta Bayesian Random-Effects Meta-Analysis and Meta-Regression | 3.3 | 3.3 |
bayesmix Bayesian Mixture Models with JAGS | 0.7-6 | 0.7-6 |
bayesmove Non-Parametric Bayesian Analyses of Animal Movement | 0.2.1 | 0.2.1 |
bayesnec A Bayesian No-Effect- Concentration (NEC) Algorithm | 2.1.1.0 | 2.1.1.0 |
BayesPiecewiseICAR Hierarchical Bayesian Model for a Hazard Function | 0.2.1 | 0.2.1 |
bayesplot Plotting for Bayesian Models | 1.11.0 | 1.11.0 |
bayesQR Bayesian Quantile Regression | 2.4 | 2.4 |
BayesSAE Bayesian Analysis of Small Area Estimation | 1.0-2 | 1.0-2 |
bayesSurv Bayesian Survival Regression with Flexible Error and Random Effects Distributions | 3.6 | 3.6 |
bayestestR Understand and Describe Bayesian Models and Posterior Distributions | 0.13.1 | 0.13.1 |
bayesTFR Bayesian Fertility Projection | 7.4-2 | 7.4-2 |
BayesTools Tools for Bayesian Analyses | 0.2.16 | 0.2.16 |
BayesTree Bayesian Additive Regression Trees | 0.3-1.5 | 0.3-1.5 |
BayesVarSel Bayes Factors, Model Choice and Variable Selection in Linear Models | 2.2.5 | 2.2.5 |
BayesX R Utilities Accompanying the Software Package BayesX | 0.3-3 | 0.3-3 |
BAYSTAR On Bayesian Analysis of Threshold Autoregressive Models | 0.2-10 | 0.2-10 |
BB Solving and Optimizing Large-Scale Nonlinear Systems | 2019.10-1 | 2019.10-1 |
bbemkr Bayesian bandwidth estimation for multivariate kernel regression<U+000a>with Gaussian error | 2.0 | 2.0 |
BBmisc Miscellaneous Helper Functions for B. Bischl | 1.13 | 1.13 |
bbmle Tools for General Maximum Likelihood Estimation | 1.0.25.1 | 1.0.25.1 |
bbotk Black-Box Optimization Toolkit | 0.7.3 | 0.7.3 |
bbricks Bayesian Methods and Graphical Model Structures for Statistical Modeling | 0.1.4 | 0.1.4 |
bcaboot Bias Corrected Bootstrap Confidence Intervals | 0.2-3 | 0.2-3 |
BCBCSF Bias-Corrected Bayesian Classification with Selected Features | 1.0-1 | 1.0-1 |
BCC1997 Calculation of Option Prices Based on a Universal Solution | 0.1.1 | 0.1.1 |
BCE Bayesian Composition Estimator: Estimating Sample (Taxonomic) Composition from Biomarker Data | 2.2.0 | 2.2.0 |
BCEA Bayesian Cost Effectiveness Analysis | 2.4.5 | 2.4.5 |
BCEE The Bayesian Causal Effect Estimation Algorithm | 1.3.2 | 1.3.2 |
bcf Causal Inference using Bayesian Causal Forests | 1.3.1 | 1.3.1 |
BCHM Clinical Trial Calculation Based on BCHM Design | 1.00 | 1.00 |
Bchron Radiocarbon Dating, Age-Depth Modelling, Relative Sea Level Rate Estimation, and Non-Parametric Phase Modelling | 4.7.6 | 4.7.6 |
bcp Bayesian Analysis of Change Point Problems | 4.0.3 | 4.0.3 |
bcpa Behavioral Change Point Analysis of Animal Movement | 1.3.2 | 1.3.2 |
bcrm Bayesian Continual Reassessment Method for Phase I Dose-Escalation Trials | 0.5.4 | 0.5.4 |
bcROCsurface Bias-Corrected Methods for Estimating the ROC Surface of Continuous Diagnostic Tests | 1.0-6 | 1.0-6 |
BDgraph Bayesian Structure Learning in Graphical Models using Birth-Death MCMC | 2.70 | 2.70 |
bdsmatrix Routines for Block Diagonal Symmetric Matrices | 1.3-6 | 1.3-6 |
beachmat | 2.14.0 | 2.14.0 |
beakr A Minimalist Web Framework for R | 0.4.3 | 0.4.3 |
beanz Bayesian Analysis of Heterogeneous Treatment Effect | 2.4 | 2.4 |
BeastJar JAR Dependency for MCMC Using 'BEAST' | 1.10.6 | 1.10.6 |
beepr Easily Play Notification Sounds on any Platform | 2.0 | 2.0 |
beeswarm The Bee Swarm Plot, an Alternative to Stripchart | 0.4.0 | 0.4.0 |
beezdemand Behavioral Economic Easy Demand | 0.1.0 | 0.1.0 |
behaviorchange Tools for Behavior Change Researchers and Professionals | 0.5.1 | 0.5.1 |
bench High Precision Timing of R Expressions | 1.1.2 | 1.1.2 |
benchden 28 Benchmark Densities from Berlinet/Devroye (1994) | 1.0.8 | 1.0.8 |
benchmarkme Crowd Sourced System Benchmarks | 1.0.8 | 1.0.8 |
benchmarkmeData Data Set for the 'benchmarkme' Package | 1.0.4 | 1.0.4 |
BenfordTests Statistical Tests for Evaluating Conformity to Benford's Law | 1.2.0 | 1.2.0 |
bentcableAR Bent-Cable Regression for Independent Data or Autoregressive Time Series | 0.3.1 | 0.3.1 |
Bergm Bayesian Exponential Random Graph Models | 5.0.7 | 5.0.7 |
berryFunctions Function Collection Related to Plotting and Hydrology | 1.22.0 | 1.22.0 |
Bessel Computations and Approximations for Bessel Functions | 0.6-0 | 0.6-0 |
BEST Bayesian Estimation Supersedes the t-Test | 0.5.4 | 0.5.4 |
BetaBit Mini Games from Adventures of Beta and Bit | 2.2 | 2.2 |
betafunctions Functions for Working with Two- And Four-Parameter Beta Probability Distributions and Psychometric Analysis of Classifications | 1.8.1 | 1.8.1 |
betareg Beta Regression | 3.1-4 | 3.1-4 |
betategarch Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCH Models | 3.3 | 3.3 |
BETS Brazilian Economic Time Series | 0.4.9 | 0.4.9 |
bets.covid19 The BETS Model for Early Epidemic Data | 1.0.0 | 1.0.0 |
bezier Toolkit for Bezier Curves and Splines | 1.1.2 | 1.1.2 |
bfast Breaks for Additive Season and Trend | 1.6.1 | 1.6.1 |
bfw Bayesian Framework for Computational Modeling | 0.4.2 | 0.4.2 |
bgmm Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling | 1.8.5 | 1.8.5 |
bgumbel Bimodal Gumbel Distribution | 0.0.3 | 0.0.3 |
BGVAR Bayesian Global Vector Autoregressions | 2.5.5 | 2.5.5 |
bgw Bunch-Gay-Welsch Statistical Estimation | 0.1.2 | 0.1.2 |
BH Boost C++ Header Files | 1.84.0-0 | 1.84.0-0 |
BHH2 Useful Functions for Box, Hunter and Hunter II | 2016.05.31 | 2016.05.31 |
bhm Biomarker Threshold Models | 1.18 | 1.18 |
BiasedUrn Biased Urn Model Distributions | 2.0.11 | 2.0.11 |
bibliometrix Comprehensive Science Mapping Analysis | 4.0.0 | 4.0.0 |
bibliometrixData Bibliometrix Example Datasets | 0.3.0 | 0.3.0 |
bibtex Bibtex Parser | 0.5.1 | 0.5.1 |
biclust BiCluster Algorithms | 2.0.3.1 | 2.0.3.1 |
biclustermd Biclustering with Missing Data | 0.2.3 | 0.2.3 |
bidask Efficient Estimation of Bid-Ask Spreads from Open, High, Low, and Close Prices | 2.0.2 | 2.0.2 |
bife Binary Choice Models with Fixed Effects | 0.7.2 | 0.7.2 |
BIFIEsurvey Tools for Survey Statistics in Educational Assessment | 3.4-15 | 3.4-15 |
bigassertr Assertion and Message Functions | 0.1.6 | 0.1.6 |
bigchess Read, Write, Manipulate, Explore Chess PGN Files and R API to UCI Chess Engines | 1.9.1 | 1.9.1 |
bigD Flexibly Format Dates and Times to a Given Locale | 0.2.0 | 0.2.0 |
bigdatadist Distances for Machine Learning and Statistics in the Context of Big Data | 1.1 | 1.1 |
bigleaf Physical and Physiological Ecosystem Properties from Eddy Covariance Data | 0.8.2 | 0.8.2 |
biglm Bounded Memory Linear and Generalized Linear Models | 0.9-2.1 | 0.9-2.1 |
biglmm Bounded Memory Linear and Generalized Linear Models | 0.9-2 | 0.9-2 |
bigmemory Manage Massive Matrices with Shared Memory and Memory-Mapped Files | 4.6.4 | 4.6.4 |
bigmemory.sri A Shared Resource Interface for Bigmemory Project Packages | 0.1.8 | 0.1.8 |
bignum Arbitrary-Precision Integer and Floating-Point Mathematics | 0.3.2 | 0.3.2 |
bigparallelr Easy Parallel Tools | 0.3.2 | 0.3.2 |
bigreadr Read Large Text Files | 0.2.5 | 0.2.5 |
bigrquery An Interface to Google's 'BigQuery' 'API' | 1.5.0 | 1.5.0 |
bigsplines Smoothing Splines for Large Samples | 1.1-1 | 1.1-1 |
bigstatsr Statistical Tools for Filebacked Big Matrices | 1.5.6 | 1.5.6 |
bigtime Sparse Estimation of Large Time Series Models | 0.2.3 | 0.2.3 |
BigVAR Dimension Reduction Methods for Multivariate Time Series | 1.1.2 | 1.1.2 |
bimets Time Series and Econometric Modeling | 3.0.2 | 3.0.2 |
bindr Parametrized Active Bindings | 0.1.1 | 0.1.1 |
bindrcpp An 'Rcpp' Interface to Active Bindings | 0.2.2 | 0.2.2 |
binman A Binary Download Manager | 0.1.3 | 0.1.3 |
binom Binomial Confidence Intervals for Several Parameterizations | 1.1-1.1 | 1.1-1.1 |
binomSamSize Confidence Intervals and Sample Size Determination for a Binomial Proportion under Simple Random Sampling and Pooled Sampling | 0.1-5 | 0.1-5 |
binr Cut Numeric Values into Evenly Distributed Groups | 1.1.1 | 1.1.1 |
binseqtest Exact Binary Sequential Designs and Analysis | 1.0.4 | 1.0.4 |
bio3d Biological Structure Analysis | 2.4-4 | 2.4-4 |
Biobase | 2.64.0 | 2.64.0 |
BiocFileCache | 2.12.0 | 2.12.0 |
BiocGenerics | 0.50.0 | 0.50.0 |
BiocIO | 1.14.0 | 1.14.0 |
BiocManager Access the Bioconductor Project Package Repository | 1.30.22 | 1.30.22 |
BiocNeighbors | 1.22.0 | 1.22.0 |
BiocParallel | 1.38.0 | 1.38.0 |
BiocSingular | 1.14.0 | 1.14.0 |
BiocVersion | 3.16.0 | 3.16.0 |
BiodiversityR Package for Community Ecology and Suitability Analysis | 2.15-2 | 2.15-2 |
bioinactivation Mathematical Modelling of (Dynamic) Microbial Inactivation | 1.2.3 | 1.2.3 |
biomaRt | 2.54.0 | 2.54.0 |
Bios2cor From Biological Sequences and Simulations to Correlation Analysis | 2.2 | 2.2 |
Biostrings | 2.72.1 | 2.72.1 |
biotic Calculation of Freshwater Biotic Indices | 0.1.2 | 0.1.2 |
bipartite Visualising Bipartite Networks and Calculating Some (Ecological) Indices | 2.19 | 2.19 |
bipd Bayesian Individual Patient Data Meta-Analysis using 'JAGS' | 0.3 | 0.3 |
birtr The R Package for "The Basics of Item Response Theory Using R" | 1.0.0 | 1.0.0 |
bit Classes and Methods for Fast Memory-Efficient Boolean Selections | 4.0.4 | 4.0.4 |
bit64 A S3 Class for Vectors of 64bit Integers | 4.0.5 | 4.0.5 |
bitops Bitwise Operations | 1.0-7 | 1.0-7 |
Bivariate.Pareto Bivariate Pareto Models | 1.0.3 | 1.0.3 |
BivarP Estimating the Parameters of Some Bivariate Distributions | 1.0 | 1.0 |
BivGeo Basu-Dhar Bivariate Geometric Distribution | 2.0.1 | 2.0.1 |
bivgeom Roy's Bivariate Geometric Distribution | 1.0 | 1.0 |
biwavelet Conduct Univariate and Bivariate Wavelet Analyses | 0.20.21 | 0.20.21 |
biwt Compute the Biweight Mean Vector and Covariance & Correlation Matrice | 1.0 | 1.0 |
bizdays Business Days Calculations and Utilities | 1.0.15 | 1.0.15 |
bjscrapeR An API Wrapper for the Bureau of Justice Statistics (BJS) | 0.1.0 | 0.1.0 |
bkmr Bayesian Kernel Machine Regression | 0.2.2 | 0.2.2 |
blaise Read and Write FWF Files in the 'Blaise' Format | 1.3.11 | 1.3.11 |
blastula Easily Send HTML Email Messages | 0.3.4 | 0.3.4 |
blavaan Bayesian Latent Variable Analysis | 0.4-6 | 0.4-6 |
blme Bayesian Linear Mixed-Effects Models | 1.0-5 | 1.0-5 |
BLModel Black-Litterman Posterior Distribution | 1.0.2 | 1.0.2 |
blob A Simple S3 Class for Representing Vectors of Binary Data ('BLOBS') | 1.2.4 | 1.2.4 |
blocklength Select an Optimal Block-Length to Bootstrap Dependent Data (Block Bootstrap) | 0.1.5 | 0.1.5 |
blockmodeling Generalized and Classical Blockmodeling of Valued Networks | 1.0.5 | 1.0.5 |
blockmodels Latent and Stochastic Block Model Estimation by a 'V-EM' Algorithm | 1.1.5 | 1.1.5 |
blockrand Randomization for Block Random Clinical Trials | 1.5 | 1.5 |
blocksdesign Nested and Crossed Block Designs for Factorial and Unstructured Treatment Sets | 4.9 | 4.9 |
blogdown Create Blogs and Websites with R Markdown | 1.17 | 1.17 |
BLOQ Impute and Analyze Data with BLOQ Observations | 0.1-1 | 0.1-1 |
blotter | 0.16.0 | 0.16.0 |
BLR Bayesian Linear Regression | 1.6 | 1.6 |
bluster | 1.14.0 | 1.14.0 |
BMA Bayesian Model Averaging | 3.18.17 | 3.18.17 |
BMAmevt Multivariate Extremes: Bayesian Estimation of the Spectral Measure | 1.0.5 | 1.0.5 |
bmgarch Bayesian Multivariate GARCH Models | 2.0.0 | 2.0.0 |
BMisc Miscellaneous Functions for Panel Data, Quantiles, and Printing Results | 1.4.5 | 1.4.5 |
Bmix Bayesian Sampling for Stick-Breaking Mixtures | 0.6 | 0.6 |
bmixture Bayesian Estimation for Finite Mixture of Distributions | 1.7 | 1.7 |
bmp Read Windows Bitmap (BMP) Images | 0.3 | 0.3 |
bmrm Bundle Methods for Regularized Risk Minimization Package | 4.1 | 4.1 |
BMS Bayesian Model Averaging Library | 0.3.5 | 0.3.5 |
BMT The BMT Distribution | 0.1.0.3 | 0.1.0.3 |
BMTAR Bayesian Approach for MTAR Models with Missing Data | 0.1.1 | 0.1.1 |
bnclassify Learning Discrete Bayesian Network Classifiers from Data | 0.4.7 | 0.4.7 |
bnlearn Bayesian Network Structure Learning, Parameter Learning and Inference | 4.9.1 | 4.9.1 |
bnma Bayesian Network Meta-Analysis using 'JAGS' | 1.5.1 | 1.5.1 |
bnnSurvival Bagged k-Nearest Neighbors Survival Prediction | 0.1.5 | 0.1.5 |
BNPTSclust A Bayesian Nonparametric Algorithm for Time Series Clustering | 2.0 | 2.0 |
BNSP Bayesian Non- And Semi-Parametric Model Fitting | 2.2.3 | 2.2.3 |
bnstruct Bayesian Network Structure Learning from Data with Missing Values | 1.0.15 | 1.0.15 |
boa Bayesian Output Analysis Program (BOA) for MCMC | 1.1.8-2 | 1.1.8-2 |
bodenmiller Profiling of Peripheral Blood Mononuclear Cells using CyTOF | 0.1.1 | 0.1.1 |
boilerpipeR Interface to the Boilerpipe Java Library | 1.3.2 | 1.3.2 |
BOIN Bayesian Optimal INterval (BOIN) Design for Single-Agent and Drug- Combination Phase I Clinical Trials | 2.7.2 | 2.7.2 |
Bolstad Functions for Elementary Bayesian Inference | 0.2-41 | 0.2-41 |
Bolstad2 Bolstad Functions | 1.0-29 | 1.0-29 |
bookdown Authoring Books and Technical Documents with R Markdown | 0.37 | 0.37 |
BoolNet Construction, Simulation and Analysis of Boolean Networks | 2.1.5 | 2.1.5 |
Boom Bayesian Object Oriented Modeling | 0.9.14 | 0.9.14 |
BoomSpikeSlab MCMC for Spike and Slab Regression | 1.2.6 | 1.2.6 |
boot Bootstrap Functions (Originally by Angelo Canty for S) | 1.3-30 | 1.3-30 |
boot.heterogeneity A Bootstrap-Based Heterogeneity Test for Meta-Analysis | 1.1.5 | 1.1.5 |
bootImpute Bootstrap Inference for Multiple Imputation | 1.2.1 | 1.2.1 |
bootnet Bootstrap Methods for Various Network Estimation Routines | 1.5.6 | 1.5.6 |
BootPR Bootstrap Prediction Intervals and Bias-Corrected Forecasting | 1.0 | 1.0 |
bootstrap Functions for the Book "An Introduction to the Bootstrap" | 2019.6 | 2019.6 |
bootUR Bootstrap Unit Root Tests | 1.0.3 | 1.0.3 |
Boptbd Bayesian Optimal Block Designs | 1.0.5 | 1.0.5 |
borrowr Estimate Causal Effects with Borrowing Between Data Sources | 0.2.0 | 0.2.0 |
Boruta Wrapper Algorithm for All Relevant Feature Selection | 8.0.0 | 8.0.0 |
boussinesq Analytic Solutions for (Ground-Water) Boussinesq Equation | 1.0.6 | 1.0.6 |
boutliers Outlier Detection and Influence Diagnostics for Meta-Analysis | 1.1-2 | 1.1-2 |
boxr Interface for the 'Box.com API' | 0.3.6 | 0.3.6 |
bpbounds Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions | 0.1.5 | 0.1.5 |
bpca Biplot of Multivariate Data Based on Principal Components Analysis | 1.3-6 | 1.3-6 |
bpcp Beta Product Confidence Procedure for Right Censored Data | 1.4.2 | 1.4.2 |
bqtl Bayesian QTL Mapping Toolkit | 1.0-36 | 1.0-36 |
BradleyTerry2 Bradley-Terry Models | 1.1-2 | 1.1-2 |
brainR Helper Functions to 'misc3d' and 'rgl' Packages for Brain Imaging | 1.6.0 | 1.6.0 |
brandwatchR 'Brandwatch' API to R | 0.3.0 | 0.3.0 |
breakDown Model Agnostic Explainers for Individual Predictions | 0.2.1 | 0.2.1 |
breakfast Methods for Fast Multiple Change-Point/Break-Point Detection and Estimation | 2.3 | 2.3 |
bReeze Functions for Wind Resource Assessment | 0.4-3 | 0.4-3 |
brew Templating Framework for Report Generation | 1.0-10 | 1.0-10 |
brglm Bias Reduction in Binomial-Response Generalized Linear Models | 0.7.2 | 0.7.2 |
brglm2 Bias Reduction in Generalized Linear Models | 0.9.2 | 0.9.2 |
bridgedist An Implementation of the Bridge Distribution with Logit-Link as in Wang and Louis (2003) | 0.1.2 | 0.1.2 |
bridgesampling Bridge Sampling for Marginal Likelihoods and Bayes Factors | 1.1-2 | 1.1-2 |
brio Basic R Input Output | 1.1.4 | 1.1.4 |
brms Bayesian Regression Models using 'Stan' | 2.20.4 | 2.20.4 |
brnn Bayesian Regularization for Feed-Forward Neural Networks | 0.9.3 | 0.9.3 |
Brobdingnag Very Large Numbers in R | 1.2-9 | 1.2-9 |
brolgar Browse Over Longitudinal Data Graphically and Analytically in R | 1.0.0 | 1.0.0 |
broman Karl Broman's R Code | 0.80 | 0.80 |
broom Convert Statistical Objects into Tidy Tibbles | 1.0.5 | 1.0.5 |
broom.helpers Helpers for Model Coefficients Tibbles | 1.14.0 | 1.14.0 |
broom.mixed Tidying Methods for Mixed Models | 0.2.9.4 | 0.2.9.4 |
broomExtra Enhancements for 'broom' and 'easystats' Package Families | 4.3.2 | 4.3.2 |
brotli A Compression Format Optimized for the Web | 1.3.0 | 1.3.0 |
brxx Bayesian Test Reliability Estimation | 0.1.2 | 0.1.2 |
bsam Bayesian State-Space Models for Animal Movement | 1.1.3 | 1.1.3 |
bsamGP Bayesian Spectral Analysis Models using Gaussian Process Priors | 1.2.4 | 1.2.4 |
BSBT The Bayesian Spatial Bradley--Terry Model | 1.2.1 | 1.2.1 |
BSDA Basic Statistics and Data Analysis | 1.2.1 | 1.2.1 |
bshazard Nonparametric Smoothing of the Hazard Function | 1.1 | 1.1 |
bslib Custom 'Bootstrap' 'Sass' Themes for 'shiny' and 'rmarkdown' | 0.6.1 | 0.6.1 |
BsMD Bayes Screening and Model Discrimination | 2023.920 | 2023.920 |
bspec Bayesian Spectral Inference | 1.6 | 1.6 |
bspm Bridge to System Package Manager | 0.5.5 | 0.5.5 |
bspmma Bayesian Semiparametric Models for Meta-Analysis | 0.1-2 | 0.1-2 |
bssm Bayesian Inference of Non-Linear and Non-Gaussian State Space Models | 2.0.2 | 2.0.2 |
BSSprep Whitening Data as Preparation for Blind Source Separation | 0.1 | 0.1 |
bst Gradient Boosting | 0.3-24 | 0.3-24 |
bsts Bayesian Structural Time Series | 0.9.10 | 0.9.10 |
BTdecayLasso Bradley-Terry Model with Exponential Time Decayed Log-Likelihood and Adaptive Lasso | 0.1.0 | 0.1.0 |
BTLLasso Modelling Heterogeneity in Paired Comparison Data | 0.1-12 | 0.1-12 |
BTM Biterm Topic Models for Short Text | 0.3.7 | 0.3.7 |
bujar Buckley-James Regression for Survival Data with High-Dimensional Covariates | 0.2-11 | 0.2-11 |
bundesbank Download Data from Bundesbank | 0.1-11 | 0.1-11 |
BurStFin Burns Statistics Financial | 1.3 | 1.3 |
BurStMisc Burns Statistics Miscellaneous | 1.1 | 1.1 |
butcher Model Butcher | 0.3.3 | 0.3.3 |
BuyseTest Generalized Pairwise Comparisons | 2.4.0 | 2.4.0 |
BVAR Hierarchical Bayesian Vector Autoregression | 1.0.4 | 1.0.4 |
bvartools Bayesian Inference of Vector Autoregressive and Error Correction Models | 0.2.4 | 0.2.4 |
bvls The Stark-Parker algorithm for bounded-variable least squares | 1.4 | 1.4 |
BWStest Baumgartner Weiss Schindler Test of Equal Distributions | 0.2.3 | 0.2.3 |
C50 C5.0 Decision Trees and Rule-Based Models | 0.1.8 | 0.1.8 |
ca Simple, Multiple and Joint Correspondence Analysis | 0.71.1 | 0.71.1 |
cabinets Project Specific Workspace Organization Templates | 0.6.0 | 0.6.0 |
cabootcrs Bootstrap Confidence Regions for Simple and Multiple Correspondence Analysis | 2.1.0 | 2.1.0 |
cachem Cache R Objects with Automatic Pruning | 1.0.8 | 1.0.8 |
cacIRT Classification Accuracy and Consistency under Item Response Theory | 1.4 | 1.4 |
CaDENCE Conditional Density Estimation Network Construction and Evaluation | 1.2.5 | 1.2.5 |
CADFtest A Package to Perform Covariate Augmented Dickey-Fuller Unit Root Tests | 0.3-3 | 0.3-3 |
caffsim Simulation of Plasma Caffeine Concentrations by Using Population Pharmacokinetic Model | 0.2.2 | 0.2.2 |
cAIC4 Conditional Akaike Information Criterion for 'lme4' and 'nlme' | 1.0 | 1.0 |
Cairo R Graphics Device using Cairo Graphics Library for Creating High-Quality Bitmap (PNG, JPEG, TIFF), Vector (PDF, SVG, PostScript) and Display (X11 and Win32) Output | 1.6-1 | 1.6-1 |
calculus High Dimensional Numerical and Symbolic Calculus | 1.0.1 | 1.0.1 |
CALIBERrfimpute Multiple Imputation Using MICE and Random Forest | 1.0-6 | 1.0-6 |
calibrate Calibration of Scatterplot and Biplot Axes | 1.7.7 | 1.7.7 |
CalibrateSSB Weighting and Estimation for Panel Data with Non-Response | 1.3.0 | 1.3.0 |
calibrator Bayesian Calibration of Complex Computer Codes | 1.2-8 | 1.2-8 |
CalibratR Mapping ML Scores to Calibrated Predictions | 0.1.2 | 0.1.2 |
callr Call R from R | 3.7.3 | 3.7.3 |
CAMAN Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN | 0.78 | 0.78 |
cancensus Access, Retrieve, and Work with Canadian Census Data and Geography | 0.5.6 | 0.5.6 |
candisc Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis | 0.8-6 | 0.8-6 |
CANSIM2R Directly Extracts Complete CANSIM Data Tables | 1.14.1 | 1.14.1 |
captr Client for the Captricity API | 0.3.0 | 0.3.0 |
car Companion to Applied Regression | 3.1-2 | 3.1-2 |
caracas Computer Algebra | 2.1.1 | 2.1.1 |
caRamel Automatic Calibration by Evolutionary Multi Objective Algorithm | 1.3 | 1.3 |
CARBayes Spatial Generalised Linear Mixed Models for Areal Unit Data | 6.1 | 6.1 |
CARBayesdata Data Used in the Vignettes Accompanying the CARBayes and CARBayesST Packages | 3.0 | 3.0 |
CARBayesST Spatio-Temporal Generalised Linear Mixed Models for Areal Unit Data | 4.0 | 4.0 |
carData Companion to Applied Regression Data Sets | 3.0-5 | 3.0-5 |
cards Analysis Results Data | 0.3.0 | 0.3.0 |
cardx Extra Analysis Results Data Utilities | 0.2.1 | 0.2.1 |
care High-Dimensional Regression and CAR Score Variable Selection | 1.1.11 | 1.1.11 |
caret Classification and Regression Training | 6.0-94 | 6.0-94 |
carfima Continuous-Time Fractionally Integrated ARMA Process for Irregularly Spaced Long-Memory Time Series Data | 2.0.2 | 2.0.2 |
caribou Estimation of Caribou Abundance Based on Radio Telemetry Data | 1.1-1 | 1.1-1 |
Carlson Carlson Elliptic Integrals and Incomplete Elliptic Integrals | 3.0.0 | 3.0.0 |
cartogram Create Cartograms with R | 0.3.0 | 0.3.0 |
cartography Thematic Cartography | 3.0.1 | 3.0.1 |
carx Censored Autoregressive Model with Exogenous Covariates | 0.7.1 | 0.7.1 |
casebase Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression | 0.10.3 | 0.10.3 |
CaseBasedReasoning Case Based Reasoning | 0.3 | 0.3 |
CAST 'caret' Applications for Spatial-Temporal Models | 0.9.0 | 0.9.0 |
cat Analysis and Imputation of Categorical-Variable Datasets with Missing Values | 0.0-9 | 0.0-9 |
catmap Case-Control and TDT Meta-Analysis Package | 1.6.4 | 1.6.4 |
caTools Tools: Moving Window Statistics, GIF, Base64, ROC AUC, etc | 1.18.2 | 1.18.2 |
catR Generation of IRT Response Patterns under Computerized Adaptive Testing | 3.17 | 3.17 |
causact Fast, Easy, and Visual Bayesian Inference | 0.5.3 | 0.5.3 |
causaldata Example Data Sets for Causal Inference Textbooks | 0.1.3 | 0.1.3 |
causaldrf Estimating Causal Dose Response Functions | 0.4.2 | 0.4.2 |
causaleffect Deriving Expressions of Joint Interventional Distributions and Transport Formulas in Causal Models | 1.3.13 | 1.3.13 |
CausalGAM Estimation of Causal Effects with Generalized Additive Models | 0.1-4 | 0.1-4 |
CausalGPS Matching on Generalized Propensity Scores with Continuous Exposures | 0.4.1 | 0.4.1 |
CausalImpact Inferring Causal Effects using Bayesian Structural Time-Series Models | 1.3.0 | 1.3.0 |
CausalMBSTS MBSTS Models for Causal Inference and Forecasting | 0.1.1 | 0.1.1 |
causaloptim An Interface to Specify Causal Graphs and Compute Bounds on Causal Effects | 0.9.8 | 0.9.8 |
causalsens Selection Bias Approach to Sensitivity Analysis for Causal Effects | 0.1.2 | 0.1.2 |
causalweight Estimation Methods for Causal Inference Based on Inverse Probability Weighting | 1.1.0 | 1.1.0 |
CAvariants Correspondence Analysis Variants | 6.0 | 6.0 |
cba Clustering for Business Analytics | 0.2-23 | 0.2-23 |
cbinom Continuous Analog of a Binomial Distribution | 1.6 | 1.6 |
CBPS Covariate Balancing Propensity Score | 0.23 | 0.23 |
cbsodataR Statistics Netherlands (CBS) Open Data API Client | 1.0.1 | 1.0.1 |
cccp Cone Constrained Convex Problems | 0.3-1 | 0.3-1 |
cclust Convex Clustering Methods and Clustering Indexes | 0.6-26 | 0.6-26 |
CCP Significance Tests for Canonical Correlation Analysis (CCA) | 1.2 | 1.2 |
cdata Fluid Data Transformations | 1.2.0 | 1.2.0 |
cdlTools Tools to Download and Work with USDA Cropscape Data | 0.15 | 0.15 |
CDM Cognitive Diagnosis Modeling | 8.2-6 | 8.2-6 |
CDNmoney Components of Canadian Monetary and Credit Aggregates | 2012.4-2 | 2012.4-2 |
cds Constrained Dual Scaling for Detecting Response Styles | 1.0.3 | 1.0.3 |
cec2013 Benchmark functions for the Special Session and Competition on Real-Parameter Single Objective Optimization at CEC-2013 | 0.1-5 | 0.1-5 |
celestial Collection of Common Astronomical Conversion Routines and Functions | 1.4.6 | 1.4.6 |
cellranger Translate Spreadsheet Cell Ranges to Rows and Columns | 1.1.0 | 1.1.0 |
cellWise Analyzing Data with Cellwise Outliers | 2.5.3 | 2.5.3 |
cem Coarsened Exact Matching | 1.1.31 | 1.1.31 |
censReg Censored Regression (Tobit) Models | 0.5-36 | 0.5-36 |
censusapi Retrieve Data from the Census APIs | 0.8.0 | 0.8.0 |
censusGeography Changes United States Census Geographic Code into Name of Location | 0.1.0 | 0.1.0 |
CEoptim Cross-Entropy R Package for Optimization | 1.3 | 1.3 |
ceterisParibus Ceteris Paribus Profiles | 0.4.2 | 0.4.2 |
cfbfastR Access College Football Play by Play Data | 1.9.0 | 1.9.0 |
CFC Cause-Specific Framework for Competing-Risk Analysis | 1.2.0 | 1.2.0 |
cfdecomp Counterfactual Decomposition: MC Integration of the G-Formula | 0.4.0 | 0.4.0 |
cfma Causal Functional Mediation Analysis | 1.0 | 1.0 |
CFtime Using CF-Compliant Calendars with Climate Projection Data | 1.4.1 | 1.4.1 |
cgdsr R-Based API for Accessing the MSKCC Cancer Genomics Data Server (CGDS) | 1.3.0 | 1.3.0 |
cglasso Conditional Graphical LASSO for Gaussian Graphical Models with Censored and Missing Values | 2.0.6 | 2.0.6 |
ChainLadder Statistical Methods and Models for Claims Reserving in General Insurance | 0.2.18 | 0.2.18 |
chandwich Chandler-Bate Sandwich Loglikelihood Adjustment | 1.1.6 | 1.1.6 |
changepoint Methods for Changepoint Detection | 2.2.4 | 2.2.4 |
changepoint.geo Geometrically Inspired Multivariate Changepoint Detection | 1.0.2 | 1.0.2 |
changepoint.mv Changepoint Analysis for Multivariate Time Series | 1.0.2 | 1.0.2 |
changepoint.np Methods for Nonparametric Changepoint Detection | 1.0.5 | 1.0.5 |
checkmate Fast and Versatile Argument Checks | 2.3.1 | 2.3.1 |
checkpoint Install Packages from Snapshots on the Checkpoint Server for Reproducibility | 1.0.2 | 1.0.2 |
chemCal Calibration Functions for Analytical Chemistry | 0.2.3 | 0.2.3 |
ChemoSpec2D Exploratory Chemometrics for 2D Spectroscopy | 0.5.0 | 0.5.0 |
ChemoSpecUtils Functions Supporting Packages ChemoSpec and ChemoSpec2D | 1.0.4 | 1.0.4 |
cherryblossom Cherry Blossom Run Race Results | 0.1.0 | 0.1.0 |
chess Read, Write, Create and Explore Chess Games | 1.0.1 | 1.0.1 |
chessR Functions to Extract, Clean and Analyse Online Chess Game Data | 1.5.2 | 1.5.2 |
chilemapas Mapas de las Divisiones Politicas y Administrativas de Chile (Maps of the Political and Administrative Divisions of Chile) | 0.3.0 | 0.3.0 |
chk Check User-Supplied Function Arguments | 0.9.1 | 0.9.1 |
CHNOSZ Thermodynamic Calculations and Diagrams for Geochemistry | 2.0.0 | 2.0.0 |
choiceDes Design Functions for Choice Studies | 0.9-3 | 0.9-3 |
CholWishart Cholesky Decomposition of the Wishart Distribution | 1.1.2 | 1.1.2 |
choroplethr Simplify the Creation of Choropleth Maps in R | 3.7.0 | 3.7.0 |
choroplethrAdmin1 Contains an Administrative-Level-1 Map of the World | 1.1.1 | 1.1.1 |
choroplethrMaps Contains Maps Used by the 'choroplethr' Package | 1.0.1 | 1.0.1 |
chromote Headless Chrome Web Browser Interface | 0.1.0 | 0.1.0 |
chron Chronological Objects which Can Handle Dates and Times | 2.3-61 | 2.3-61 |
CHsharp Choi and Hall Style Data Sharpening | 0.4 | 0.4 |
chyper Functions for Conditional Hypergeometric Distributions | 0.3.1 | 0.3.1 |
CIAAWconsensus Isotope Ratio Meta-Analysis | 1.3 | 1.3 |
CIEE Estimating and Testing Direct Effects in Directed Acyclic Graphs using Estimating Equations | 0.1.1 | 0.1.1 |
cifti Toolbox for Connectivity Informatics Technology Initiative ('CIFTI') Files | 0.4.5 | 0.4.5 |
cinterpolate Interpolation From C | 1.0.1 | 1.0.1 |
circlize Circular Visualization | 0.4.15 | 0.4.15 |
CircSpaceTime Spatial and Spatio-Temporal Bayesian Model for Circular Data | 0.9.0 | 0.9.0 |
CircStats Circular Statistics, from "Topics in Circular Statistics" (2001) | 0.2-6 | 0.2-6 |
circular Circular Statistics | 0.5-0 | 0.5-0 |
circumplex Analysis and Visualization of Circular Data | 0.3.10 | 0.3.10 |
cit Causal Inference Test | 2.3.1 | 2.3.1 |
citationchaser Perform Forward and Backwards Chasing in Evidence Syntheses | 0.0.4 | 0.0.4 |
ciTools Confidence or Prediction Intervals, Quantiles, and Probabilities for Statistical Models | 0.6.1 | 0.6.1 |
cjoint AMCE Estimator for Conjoint Experiments | 2.1.1 | 2.1.1 |
CKAT Composite Kernel Association Test for Pharmacogenetics Studies | 0.1.0 | 0.1.0 |
Ckmeans.1d.dp Optimal, Fast, and Reproducible Univariate Clustering | 4.3.4 | 4.3.4 |
clarabel Interior Point Conic Optimization Solver | 0.9.0.1 | 0.9.0.1 |
clarifai Access to Clarifai API | 0.4.2 | 0.4.2 |
class Functions for Classification | 7.3-22 | 7.3-22 |
classInt Choose Univariate Class Intervals | 0.4-10 | 0.4-10 |
cli Helpers for Developing Command Line Interfaces | 3.6.2 | 3.6.2 |
cliapp Create Rich Command Line Applications | 0.1.1 | 0.1.1 |
clifford Arbitrary Dimensional Clifford Algebras | 1.0-8 | 1.0-8 |
clifro Easily Download and Visualise Climate Data from CliFlo | 3.2-5 | 3.2-5 |
climate Interface to Download Meteorological (and Hydrological) Datasets | 1.0.5 | 1.0.5 |
climatol Climate Tools (Series Homogenization and Derived Products) | 4.0.0 | 4.0.0 |
climdex.pcic PCIC Implementation of Climdex Routines | 1.1-11 | 1.1-11 |
clime Constrained L1-Minimization for Inverse (Covariance) Matrix Estimation | 0.5.0 | 0.5.0 |
climextRemes Tools for Analyzing Climate Extremes | 0.3.1 | 0.3.1 |
clinfun Clinical Trial Design and Data Analysis Functions | 1.1.5 | 1.1.5 |
clinPK Clinical Pharmacokinetics Toolkit | 0.11.1 | 0.11.1 |
clinsig Clinical Significance Functions | 1.2 | 1.2 |
clipr Read and Write from the System Clipboard | 0.8.0 | 0.8.0 |
clisymbols Unicode Symbols at the R Prompt | 1.2.0 | 1.2.0 |
clock Date-Time Types and Tools | 0.7.0 | 0.7.0 |
cloudml Interface to the Google Cloud Machine Learning Platform | 0.6.1 | 0.6.1 |
clubSandwich Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections | 0.5.10 | 0.5.10 |
clue Cluster Ensembles | 0.3-65 | 0.3-65 |
cluster "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al. | 2.1.3 | 2.1.3 |
clusterCrit Clustering Indices | 1.3.0 | 1.3.0 |
clusteredinterference Causal Effects from Observational Studies with Clustered Interference | 1.0.1 | 1.0.1 |
clusterGeneration Random Cluster Generation (with Specified Degree of Separation) | 1.3.8 | 1.3.8 |
clustermq Evaluate Function Calls on HPC Schedulers (LSF, SGE, SLURM, PBS/Torque) | 0.9.3 | 0.9.3 |
clusterPower Power Calculations for Cluster-Randomized and Cluster-Randomized Crossover Trials | 0.7.0 | 0.7.0 |
clusterProfiler | 4.12.6 | 4.12.6 |
ClusterR Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering | 1.3.2 | 1.3.2 |
clusterRepro Reproducibility of Gene Expression Clusters | 0.9 | 0.9 |
clusterSEs Calculate Cluster-Robust p-Values and Confidence Intervals | 2.6.5 | 2.6.5 |
clusterSim Searching for Optimal Clustering Procedure for a Data Set | 0.51-3 | 0.51-3 |
ClustImpute K-Means Clustering with Build-in Missing Data Imputation | 0.2.4 | 0.2.4 |
clustMixType k-Prototypes Clustering for Mixed Variable-Type Data | 0.3-14 | 0.3-14 |
ClustVarLV Clustering of Variables Around Latent Variables | 2.1.1 | 2.1.1 |
clustvarsel Variable Selection for Gaussian Model-Based Clustering | 2.3.4 | 2.3.4 |
clv Cluster Validation Techniques | 0.3-2.4 | 0.3-2.4 |
clValid Validation of Clustering Results | 0.7 | 0.7 |
cmaes Covariance Matrix Adapting Evolutionary Strategy | 1.0-12 | 1.0-12 |
cmaesr Covariance Matrix Adaptation Evolution Strategy | 1.0.3 | 1.0.3 |
CMC Cronbach-Mesbah Curve | 1.0 | 1.0 |
CMF Collective Matrix Factorization | 1.0.3 | 1.0.3 |
cmfrec Collective Matrix Factorization for Recommender Systems | 3.5.1-3 | 3.5.1-3 |
CMLS Constrained Multivariate Least Squares | 1.0-1 | 1.0-1 |
cmm Categorical Marginal Models | 1.0 | 1.0 |
cmocean Beautiful Colour Maps for Oceanography | 0.3-1 | 0.3-1 |
cmprsk Subdistribution Analysis of Competing Risks | 2.2-11 | 2.2-11 |
cmprskQR Analysis of Competing Risks Using Quantile Regressions | 0.9.2 | 0.9.2 |
cmrutils Misc Functions of the Center for Mathematical Research | 1.3.1 | 1.3.1 |
cmstatr Statistical Methods for Composite Material Data | 0.9.1 | 0.9.1 |
cmvnorm The Complex Multivariate Gaussian Distribution | 1.0-7 | 1.0-7 |
cna Causal Modeling with Coincidence Analysis | 3.5.6 | 3.5.6 |
cncaGUI Canonical Non-Symmetrical Correspondence Analysis in R | 1.1 | 1.1 |
cNORM Continuous Norming | 3.0.4 | 3.0.4 |
coalescentMCMC MCMC Algorithms for the Coalescent | 0.4-4 | 0.4-4 |
coalitions Bayesian "Now-Cast" Estimation of Event Probabilities in Multi-Party Democracies | 0.6.24 | 0.6.24 |
coarseDataTools Analysis of Coarsely Observed Data | 0.6-6 | 0.6-6 |
cobalt Covariate Balance Tables and Plots | 4.5.3 | 4.5.3 |
cobs Constrained B-Splines (Sparse Matrix Based) | 1.3-5 | 1.3-5 |
CoClust Copula Based Cluster Analysis | 0.3-2 | 0.3-2 |
COCONUT COmbat CO-Normalization Using conTrols (COCONUT) | 1.0.2 | 1.0.2 |
cocor Comparing Correlations | 1.1-4 | 1.1-4 |
cocorresp Co-Correspondence Analysis Methods | 0.4-3 | 0.4-3 |
cocron Statistical Comparisons of Two or more Alpha Coefficients | 1.0-1 | 1.0-1 |
coda Output Analysis and Diagnostics for MCMC | 0.19-4 | 0.19-4 |
codalm Transformation-Free Linear Regression for Compositional Outcomes and Predictors | 0.1.2 | 0.1.2 |
cOde Automated C Code Generation for 'deSolve', 'bvpSolve' | 1.1.1 | 1.1.1 |
codetools Code Analysis Tools for R | 0.2-19 | 0.2-19 |
coefplot Plots Coefficients from Fitted Models | 1.2.8 | 1.2.8 |
coga Convolution of Gamma Distributions | 1.2.2 | 1.2.2 |
CoImp Parametric and Non-Parametric Copula-Based Imputation Methods | 1.0 | 1.0 |
coin Conditional Inference Procedures in a Permutation Test Framework | 1.4-3 | 1.4-3 |
cointReg Parameter Estimation and Inference in a Cointegrating Regression | 0.2.0 | 0.2.0 |
cold Count Longitudinal Data | 2.0-3 | 2.0-3 |
colf Constrained Optimization on Linear Function | 0.1.3 | 0.1.3 |
collapse Advanced and Fast Data Transformation | 2.0.16 | 2.0.16 |
collapsibleTree Interactive Collapsible Tree Diagrams using 'D3.js' | 0.1.8 | 0.1.8 |
collections High Performance Container Data Types | 0.3.5 | 0.3.5 |
CollocInfer Collocation Inference for Dynamic Systems | 1.0.4 | 1.0.4 |
colorr Color Palettes for EPL, MLB, NBA, NHL, and NFL Teams | 1.0.0 | 1.0.0 |
colorRamps Builds Color Tables | 2.3.1 | 2.3.1 |
colorspace A Toolbox for Manipulating and Assessing Colors and Palettes | 2.1-0 | 2.1-0 |
colourpicker A Colour Picker Tool for Shiny and for Selecting Colours in Plots | 1.3.0 | 1.3.0 |
colourvalues Assigns Colours to Values | 0.3.9 | 0.3.9 |
combinat combinatorics utilities | 0.0-8 | 0.0-8 |
combinedevents Calculate Scores and Marks for Track and Field Combined Events | 0.1.1 | 0.1.1 |
CombinS Construction Methods of some Series of PBIB Designs | 1.1-1 | 1.1-1 |
CommonJavaJars Useful Libraries for Building a Java Based GUI under R | 1.0-6 | 1.0-6 |
commonmark High Performance CommonMark and Github Markdown Rendering in R | 1.9.1 | 1.9.1 |
compare Comparing Objects for Differences | 0.2-6 | 0.2-6 |
compareC Compare Two Correlated C Indices with Right-Censored Survival Outcome | 1.3.2 | 1.3.2 |
CompareCausalNetworks Interface to Diverse Estimation Methods of Causal Networks | 0.2.6.2 | 0.2.6.2 |
compareGroups Descriptive Analysis by Groups | 4.5.1 | 4.5.1 |
competitiontoolbox A Graphical User Interface for Antitrust and Trade Practitioners | 0.7.0 | 0.7.0 |
compHclust Complementary Hierarchical Clustering | 1.0-3 | 1.0-3 |
compiler | 4.4.1 | 4.4.1 |
ComplexUpset Create Complex UpSet Plots Using 'ggplot2' Components | 1.3.3 | 1.3.3 |
complmrob Robust Linear Regression with Compositional Data as Covariates | 0.7.0 | 0.7.0 |
CompLognormal Functions for actuarial scientists | 3.0 | 3.0 |
Compositional Compositional Data Analysis | 6.6 | 6.6 |
compositions Compositional Data Analysis | 2.0-8 | 2.0-8 |
compound.Cox Univariate Feature Selection and Compound Covariate for Predicting Survival, Including Copula-Based Analyses for Dependent Censoring | 3.30 | 3.30 |
Compounding Computing Continuous Distributions | 1.0.2 | 1.0.2 |
CompQuadForm Distribution Function of Quadratic Forms in Normal Variables | 1.4.3 | 1.4.3 |
compute.es Compute Effect Sizes | 0.2-5 | 0.2-5 |
concreg Concordance Regression | 0.7 | 0.7 |
concurve Computes & Plots Compatibility (Confidence), Surprisal, & Likelihood Distributions | 2.7.7 | 2.7.7 |
condGEE Parameter Estimation in Conditional GEE for Recurrent Event Gap Times | 0.2.0 | 0.2.0 |
CondIndTests Nonlinear Conditional Independence Tests | 0.1.5 | 0.1.5 |
condMVNorm Conditional Multivariate Normal Distribution | 2020.1 | 2020.1 |
condSURV Estimation of the Conditional Survival Function for Ordered Multivariate Failure Time Data | 2.0.4 | 2.0.4 |
coneproj Primal or Dual Cone Projections with Routines for Constrained Regression | 1.17 | 1.17 |
conf.design Construction of factorial designs | 2.0.0 | 2.0.0 |
config Manage Environment Specific Configuration Values | 0.3.2 | 0.3.2 |
confintr Confidence Intervals | 1.0.2 | 1.0.2 |
conflicted An Alternative Conflict Resolution Strategy | 1.2.0 | 1.2.0 |
confoundr Diagnostics for Confounding of Time-Varying and Other Joint Exposures | 1.2 | 1.2 |
conicfit Algorithms for Fitting Circles, Ellipses and Conics Based on the Work by Prof. Nikolai Chernov | 1.0.4 | 1.0.4 |
conjoint An Implementation of Conjoint Analysis Method | 1.41 | 1.41 |
conquer Convolution-Type Smoothed Quantile Regression | 1.3.3 | 1.3.3 |
conquestr An R Package to Extend 'ACER ConQuest' | 1.1.1 | 1.1.1 |
ConsRank Compute the Median Ranking(s) According to the Kemeny's Axiomatic Approach | 2.1.4 | 2.1.4 |
constants Reference on Constants, Units and Uncertainty | 1.0.1 | 1.0.1 |
contactdata Social Contact Matrices for 177 Countries | 1.0.0 | 1.0.0 |
container Extending Base 'R' Lists | 1.0.2 | 1.0.2 |
contfrac Continued Fractions | 1.1-12 | 1.1-12 |
conting Bayesian Analysis of Contingency Tables | 1.7 | 1.7 |
controlTest Quantile Comparison for Two-Sample Right-Censored Survival Data | 1.1.0 | 1.1.0 |
convergEU Monitoring Convergence of EU Countries | 0.5.4 | 0.5.4 |
convey Income Concentration Analysis with Complex Survey Samples | 1.0.0 | 1.0.0 |
coop Co-Operation: Fast Covariance, Correlation, and Cosine Similarity Operations | 0.6-3 | 0.6-3 |
copBasic General Bivariate Copula Theory and Many Utility Functions | 2.2.3 | 2.2.3 |
cops Cluster Optimized Proximity Scaling | 1.12-1 | 1.12-1 |
copula Multivariate Dependence with Copulas | 1.1-3 | 1.1-3 |
copulaData Data Sets for Copula Modeling | 0.0-1 | 0.0-1 |
CopulaDTA Copula Based Bivariate Beta-Binomial Model for Diagnostic Test Accuracy Studies | 1.0.0 | 1.0.0 |
copulaedas Estimation of Distribution Algorithms Based on Copulas | 1.4.3 | 1.4.3 |
cordillera Calculation of the OPTICS Cordillera | 1.0-0 | 1.0-0 |
CORElearn Classification, Regression and Feature Evaluation | 1.57.3 | 1.57.3 |
cornet Penalised Regression for Dichotomised Outcomes | 0.0.9 | 0.0.9 |
coro 'Coroutines' for R | 1.0.3 | 1.0.3 |
corona Coronavirus ('Rona') Data Exploration | 0.3.0 | 0.3.0 |
coronavirus The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset | 0.4.1 | 0.4.1 |
corpcor Efficient Estimation of Covariance and (Partial) Correlation | 1.6.10 | 1.6.10 |
corpora Statistics and Data Sets for Corpus Frequency Data | 0.6 | 0.6 |
corporaexplorer A 'Shiny' App for Exploration of Text Collections | 0.8.6 | 0.8.6 |
correlation Methods for Correlation Analysis | 0.8.5 | 0.8.5 |
corrplot Visualization of a Correlation Matrix | 0.92 | 0.92 |
cort Some Empiric and Nonparametric Copula Models | 0.3.2 | 0.3.2 |
cosa Bound Constrained Optimal Sample Size Allocation | 2.1.0 | 2.1.0 |
cosinor Tools for Estimating and Predicting the Cosinor Model | 1.2.2 | 1.2.2 |
cosinor2 Extended Tools for Cosinor Analysis of Rhythms | 0.2.1 | 0.2.1 |
cosmoFns Functions for Cosmological Distances, Times, Luminosities, Etc | 1.1-1 | 1.1-1 |
CoSMoS Complete Stochastic Modelling Solution | 2.1.0 | 2.1.0 |
costat Time Series Costationarity Determination | 2.4.1 | 2.4.1 |
Counterfactual Estimation and Inference Methods for Counterfactual Analysis | 1.2 | 1.2 |
countrycode Convert Country Names and Country Codes | 1.5.0 | 1.5.0 |
COVID19 R Interface to COVID-19 Data Hub | 3.0.3 | 3.0.3 |
covid19.analytics Load and Analyze Live Data from the COVID-19 Pandemic | 2.1.3.3 | 2.1.3.3 |
covid19br Brazilian COVID-19 Pandemic Data | 0.1.8 | 0.1.8 |
covid19dbcand Selected 'Drugbank' Drugs for COVID-19 Treatment Related Data in R Format | 0.1.1 | 0.1.1 |
covid19france Cases of COVID-19 in France | 0.1.0 | 0.1.0 |
covid19italy The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Dataset | 0.3.1 | 0.3.1 |
covid19sf The Covid19 San Francisco Dataset | 0.1.2 | 0.1.2 |
covid19swiss COVID-19 Cases in Switzerland and Principality of Liechtenstein | 0.1.0 | 0.1.0 |
covid19us Cases of COVID-19 in the United States | 0.1.9 | 0.1.9 |
CovidMutations Mutation Analysis Toolkit for COVID-19 (Coronavirus Disease 2019) | 0.1.3 | 0.1.3 |
covLCA Latent Class Models with Covariate Effects on Underlying and<U+000a>Measured Variables | 1.0 | 1.0 |
covr Test Coverage for Packages | 3.6.4 | 3.6.4 |
covRobust Robust Covariance Estimation via Nearest Neighbor Cleaning | 1.1-3 | 1.1-3 |
covsep Tests for Determining if the Covariance Structure of 2-Dimensional Data is Separable | 1.1.0 | 1.1.0 |
cowplot Streamlined Plot Theme and Plot Annotations for 'ggplot2' | 1.1.3 | 1.1.3 |
CoxBoost Cox models by likelihood based boosting for a single survival<U+000a>endpoint or competing risks | 1.4 | 1.4 |
coxed Duration-Based Quantities of Interest for the Cox Proportional Hazards Model | 0.3.3 | 0.3.3 |
coxinterval Cox-Type Models for Interval-Censored Data | 1.2 | 1.2 |
coxme Mixed Effects Cox Models | 2.2-18.1 | 2.2-18.1 |
coxphf Cox Regression with Firth's Penalized Likelihood | 1.13.4 | 1.13.4 |
coxphw Weighted Estimation in Cox Regression | 4.0.3 | 4.0.3 |
coxrobust Fit Robustly Proportional Hazards Regression Model | 1.0.1 | 1.0.1 |
coxsei Fitting a CoxSEI Model | 0.3 | 0.3 |
CPBayes Bayesian Meta Analysis for Studying Cross-Phenotype Genetic Associations | 1.1.0 | 1.1.0 |
cpk Clinical Pharmacokinetics | 1.3-1 | 1.3-1 |
cplm Compound Poisson Linear Models | 0.7-12 | 0.7-12 |
cpp11 A C++11 Interface for R's C Interface | 0.4.7 | 0.4.7 |
cpp11armadillo An 'Armadillo' Interface | 0.3.3 | 0.3.3 |
Cprob The Conditional Probability Function of a Competing Event | 1.4.1 | 1.4.1 |
CRAC Cosmology R Analysis Code | 1.0 | 1.0 |
crawl Fit Continuous-Time Correlated Random Walk Models to Animal Movement Data | 2.2.1 | 2.2.1 |
crayon Colored Terminal Output | 1.5.2 | 1.5.2 |
crch Censored Regression with Conditional Heteroscedasticity | 1.1-2 | 1.1-2 |
credentials Tools for Managing SSH and Git Credentials | 2.0.1 | 2.0.1 |
credule Credit Default Swap Functions | 0.1.4 | 0.1.4 |
crfsuite Conditional Random Fields for Labelling Sequential Data in Natural Language Processing | 0.4.2 | 0.4.2 |
cricketdata International Cricket Data | 0.2.3 | 0.2.3 |
cricketr Analyze Cricketers and Cricket Teams Based on ESPN Cricinfo Statsguru | 0.0.26 | 0.0.26 |
crimCV Group-Based Modelling of Longitudinal Data | 1.0.0 | 1.0.0 |
CRM Continual Reassessment Method (CRM) for Phase I Clinical Trials | 1.2.4 | 1.2.4 |
crmPack Object-Oriented Implementation of CRM Designs | 1.0.4 | 1.0.4 |
crossdes Construction of Crossover Designs | 1.1-2 | 1.1-2 |
Crossover Analysis and Search of Crossover Designs | 0.1-21 | 0.1-21 |
crosstable Crosstables for Descriptive Analyses | 0.8.0 | 0.8.0 |
crosstalk Inter-Widget Interactivity for HTML Widgets | 1.2.1 | 1.2.1 |
crrSC Competing Risks Regression for Stratified and Clustered Data | 1.1.2 | 1.1.2 |
crrstep Stepwise Covariate Selection for the Fine & Gray Competing Risks Regression Model | 2015-2.1 | 2015-2.1 |
crs Categorical Regression Splines | 0.15-37 | 0.15-37 |
crseEventStudy A Robust and Powerful Test of Abnormal Stock Returns in Long-Horizon Event Studies | 1.2.2 | 1.2.2 |
crsmeta Extract Coordinate System Metadata | 0.3.0 | 0.3.0 |
CRTgeeDR Doubly Robust Inverse Probability Weighted Augmented GEE Estimator | 2.0.1 | 2.0.1 |
CRTSize Sample Size Estimation Functions for Cluster Randomized Trials | 1.2 | 1.2 |
crul HTTP Client | 1.4.0 | 1.4.0 |
crunch Crunch.io Data Tools | 1.30.2 | 1.30.2 |
crunchy Shiny Apps on Crunch | 0.3.3 | 0.3.3 |
csci Current Status Confidence Intervals | 0.9.3 | 0.9.3 |
CSGo Collecting Counter Strike Global Offensive Data | 0.6.7 | 0.6.7 |
cshapes The CShapes 2.0 Dataset and Utilities | 2.0 | 2.0 |
csn Closed Skew-Normal Distribution | 1.1.3 | 1.1.3 |
csodata Download Data from the CSO 'PxStat' API | 1.4.2 | 1.4.2 |
cstab Selection of Number of Clusters via Normalized Clustering Instability | 0.2-2 | 0.2-2 |
ctmcd Estimating the Parameters of a Continuous-Time Markov Chain from Discrete-Time Data | 1.4.1 | 1.4.1 |
ctmcmove Modeling Animal Movement with Continuous-Time Discrete-Space Markov Chains | 1.2.9 | 1.2.9 |
ctmle Collaborative Targeted Maximum Likelihood Estimation | 0.1.2 | 0.1.2 |
ctmm Continuous-Time Movement Modeling | 1.2.0 | 1.2.0 |
ctsem Continuous Time Structural Equation Modelling | 3.10.1 | 3.10.1 |
CTT Classical Test Theory Functions | 2.3.3 | 2.3.3 |
CTTShiny Classical Test Theory via Shiny | 0.1 | 0.1 |
cubature Adaptive Multivariate Integration over Hypercubes | 2.1.0 | 2.1.0 |
cubble A Vector Spatio-Temporal Data Structure for Data Analysis | 1.0.0 | 1.0.0 |
cubelyr A Data Cube 'dplyr' Backend | 1.0.2 | 1.0.2 |
Cubist Rule- And Instance-Based Regression Modeling | 0.4.2.1 | 0.4.2.1 |
curl A Modern and Flexible Web Client for R | 5.0.2 | 5.0.2 |
currentSurvival Estimation of CCI and CLFS Functions | 1.1 | 1.1 |
cutoffR CUTOFF: A Spatio-temporal Imputation Method | 1.0 | 1.0 |
cutpointr Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks | 1.1.1 | 1.1.1 |
cvar Compute Expected Shortfall and Value at Risk for Continuous Distributions | 0.5 | 0.5 |
cvAUC Cross-Validated Area Under the ROC Curve Confidence Intervals | 1.1.4 | 1.1.4 |
CVST Fast Cross-Validation via Sequential Testing | 0.2-3 | 0.2-3 |
cvTools Cross-Validation Tools for Regression Models | 0.3.2 | 0.3.2 |
CVXR Disciplined Convex Optimization | 1.0-11 | 1.0-11 |
cyclestreets Cycle Routing and Data for Cycling Advocacy | 0.5.3 | 0.5.3 |
cyclocomp Cyclomatic Complexity of R Code | 1.1.0 | 1.1.0 |
Cyclops Cyclic Coordinate Descent for Logistic, Poisson and Survival Analysis | 3.4.0 | 3.4.0 |
cytofan Plot Fan Plots for Cytometry Data using 'ggplot2' | 0.1.0 | 0.1.0 |
d3Network Tools for creating D3 JavaScript network, tree, dendrogram, and<U+000a>Sankey graphs from R | 0.5.2.1 | 0.5.2.1 |
DAAG Data Analysis and Graphics Data and Functions | 1.25.4 | 1.25.4 |
daarem Damped Anderson Acceleration with Epsilon Monotonicity for Accelerating EM-Like Monotone Algorithms | 0.7 | 0.7 |
dae Functions Useful in the Design and ANOVA of Experiments | 3.2.21 | 3.2.21 |
daewr Design and Analysis of Experiments with R | 1.2-11 | 1.2-11 |
dagitty Graphical Analysis of Structural Causal Models | 0.3-1 | 0.3-1 |
DAKS Data Analysis and Knowledge Spaces | 2.1-3 | 2.1-3 |
DALEX moDel Agnostic Language for Exploration and eXplanation | 2.4.3 | 2.4.3 |
dalmatian Automating the Fitting of Double Linear Mixed Models in 'JAGS' and 'nimble' | 1.0.0 | 1.0.0 |
dash An Interface to the Dash Ecosystem for Authoring Reactive Web Applications | 0.9.4 | 0.9.4 |
data.table Extension of `data.frame` | 1.15.0 | 1.15.0 |
data.tree General Purpose Hierarchical Data Structure | 1.1.0 | 1.1.0 |
DatabionicSwarm Swarm Intelligence for Self-Organized Clustering | 1.2.1 | 1.2.1 |
dataone R Interface to the DataONE REST API | 2.2.2 | 2.2.2 |
datapack A Flexible Container to Transport and Manipulate Data and Associated Resources | 1.4.1 | 1.4.1 |
dataRetrieval Retrieval Functions for USGS and EPA Hydrology and Water Quality Data | 2.7.14 | 2.7.14 |
datarobot 'DataRobot' Predictive Modeling API | 2.18.5 | 2.18.5 |
dataseries Switzerland's Data Series in One Place | 0.2.0 | 0.2.0 |
datasets | 4.4.1 | 4.4.1 |
dataverse Client for Dataverse 4+ Repositories | 0.3.13 | 0.3.13 |
DataVisualizations Visualizations of High-Dimensional Data | 1.3.2 | 1.3.2 |
datawizard Easy Data Wrangling and Statistical Transformations | 0.13.0 | 0.13.0 |
date Functions for Handling Dates | 1.2-39 | 1.2-39 |
datetimeutils Utilities for Dates and Times | 0.6-3 | 0.6-3 |
Davies The Davies Quantile Function | 1.2-0 | 1.2-0 |
dbarts Discrete Bayesian Additive Regression Trees Sampler | 0.9-25 | 0.9-25 |
DBI R Database Interface | 1.2.1 | 1.2.1 |
DBItest Testing DBI Backends | 1.8.0 | 1.8.0 |
dblcens Compute the NPMLE of Distribution Function from Doubly Censored Data, Plus the Empirical Likelihood Ratio for F(T) | 1.1.9 | 1.1.9 |
dbmss Distance-Based Measures of Spatial Structures | 2.9-0 | 2.9-0 |
dbparser Drugs Databases Parser | 2.0.1 | 2.0.1 |
dbplyr A 'dplyr' Back End for Databases | 2.4.0 | 2.4.0 |
dbscan Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms | 1.1-12 | 1.1-12 |
dbx A Fast, Easy-to-Use Database Interface | 0.3.1 | 0.3.1 |
DChaos Chaotic Time Series Analysis | 0.1-7 | 0.1-7 |
dclone Data Cloning and MCMC Tools for Maximum Likelihood Methods | 2.3-0 | 2.3-0 |
DCluster Functions for the Detection of Spatial Clusters of Diseases | 0.2-10 | 0.2-10 |
DClusterm Model-Based Detection of Disease Clusters | 1.0-1 | 1.0-1 |
dcov A Fast Implementation of Distance Covariance | 0.1.1 | 0.1.1 |
dCovTS Distance Covariance and Correlation for Time Series Analysis | 1.4 | 1.4 |
dcurver Utility Functions for Davidian Curves | 0.9.2 | 0.9.2 |
ddalpha Depth-Based Classification and Calculation of Data Depth | 1.3.15 | 1.3.15 |
ddCt | 1.54.0 | 1.54.0 |
dde Solve Delay Differential Equations | 1.0.5 | 1.0.5 |
DDRTree Learning Principal Graphs with DDRTree | 0.1.5 | 0.1.5 |
deal Learning Bayesian Networks with Mixed Variables | 1.2-39 | 1.2-39 |
deBInfer Bayesian Inference for Differential Equations | 0.4.4 | 0.4.4 |
debugme Debug R Packages | 1.1.0 | 1.1.0 |
DeclareDesign Declare and Diagnose Research Designs | 1.0.0 | 1.0.0 |
decompr Global Value Chain Decomposition | 6.4.0 | 6.4.0 |
decor Retrieve Code Decorations | 1.0.1 | 1.0.1 |
deducorrect Deductive Correction, Deductive Imputation, and Deterministic Correction | 1.3.7 | 1.3.7 |
deductive Data Correction and Imputation Using Deductive Methods | 1.0.0 | 1.0.0 |
deepnet Deep Learning Toolkit in R | 0.2.1 | 0.2.1 |
degreenet Models for Skewed Count Distributions Relevant to Networks | 1.3-3 | 1.3-3 |
dejaVu Multiple Imputation for Recurrent Events | 0.3.0 | 0.3.0 |
Delaporte Statistical Functions for the Delaporte Distribution | 8.3.0 | 8.3.0 |
DelayedArray | 0.24.0 | 0.24.0 |
DelayedMatrixStats | 1.20.0 | 1.20.0 |
deldir Delaunay Triangulation and Dirichlet (Voronoi) Tessellation | 2.0-2 | 2.0-2 |
deltaPlotR Identification of Dichotomous Differential Item Functioning (DIF) using Angoff's Delta Plot Method | 1.6 | 1.6 |
dendextend Extending 'dendrogram' Functionality in R | 1.17.1 | 1.17.1 |
denoiseR Regularized Low Rank Matrix Estimation | 1.0.2 | 1.0.2 |
denseFLMM Functional Linear Mixed Models for Densely Sampled Data | 0.1.2 | 0.1.2 |
densEstBayes Density Estimation via Bayesian Inference Engines | 1.0-2.2 | 1.0-2.2 |
densityClust Clustering by Fast Search and Find of Density Peaks | 0.3 | 0.3 |
denstrip Density Strips and Other Methods for Compactly Illustrating Distributions | 1.5.4 | 1.5.4 |
DEoptim Global Optimization by Differential Evolution | 2.2-8 | 2.2-8 |
DEoptimR Differential Evolution Optimization in Pure R | 1.1-3 | 1.1-3 |
depmix Dependent Mixture Models | 0.9.16 | 0.9.16 |
depmixS4 Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4 | 1.5-0 | 1.5-0 |
DepthProc Statistical Depth Functions for Multivariate Analysis | 2.1.5 | 2.1.5 |
Deriv Symbolic Differentiation | 4.1.3 | 4.1.3 |
derivmkts Functions and R Code to Accompany Derivatives Markets | 0.2.5 | 0.2.5 |
desc Manipulate DESCRIPTION Files | 1.4.3 | 1.4.3 |
DescTools Tools for Descriptive Statistics | 0.99.53 | 0.99.53 |
DESeq2 | 1.38.2 | 1.38.2 |
designGG Computational tool for designing genetical genomics experiments. | 1.1 | 1.1 |
DesignLibrary Library of Research Designs | 0.1.10 | 0.1.10 |
designmatch Matched Samples that are Balanced and Representative by Design | 0.4.1 | 0.4.1 |
desirability Function Optimization and Ranking via Desirability Functions | 2.1 | 2.1 |
deSolve Solvers for Initial Value Problems of Differential Equations ('ODE', 'DAE', 'DDE') | 1.40 | 1.40 |
desplot Plotting Field Plans for Agricultural Experiments | 1.10 | 1.10 |
details Create Details HTML Tag for Markdown and Package Documentation | 0.3.0 | 0.3.0 |
detpack Density Estimation and Random Number Generation with Distribution Element Trees | 1.1.3 | 1.1.3 |
devEMF EMF Graphics Output Device | 4.5 | 4.5 |
devtools Tools to Make Developing R Packages Easier | 2.4.4 | 2.4.4 |
dexter Data Management and Analysis of Tests | 1.4.0 | 1.4.0 |
dextergui A Graphical User Interface for Dexter | 0.2.6 | 0.2.6 |
dexterMST CML and Bayesian Calibration of Multistage Tests | 0.9.6 | 0.9.6 |
dfcomb Phase I/II Adaptive Dose-Finding Design for Combination Studies | 3.1-1 | 3.1-1 |
dfcrm Dose-Finding by the Continual Reassessment Method | 0.2-2.1 | 0.2-2.1 |
dfidx Indexed Data Frames | 0.0-5 | 0.0-5 |
DFIT Differential Functioning of Items and Tests | 1.1 | 1.1 |
dfmeta Meta-Analysis of Phase I Dose-Finding Early Clinical Trials | 1.0.0 | 1.0.0 |
dfmta Phase I/II Adaptive Dose-Finding Design for MTA | 1.7-3 | 1.7-3 |
dfoptim Derivative-Free Optimization | 2023.1.0 | 2023.1.0 |
dfped Extrapolation and Bridging of Adult Information in Early Phase Dose-Finding Paediatrics Studies | 1.1 | 1.1 |
dfpk Bayesian Dose-Finding Designs using Pharmacokinetics (PK) for Phase I Clinical Trials | 3.5.1 | 3.5.1 |
dggridR Discrete Global Grids | 3.1.0 | 3.1.0 |
dglm Double Generalized Linear Models | 1.8.4 | 1.8.4 |
dgumbel The Gumbel Distribution Functions and Gradients | 1.0.1 | 1.0.1 |
DHARMa Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models | 0.4.6 | 0.4.6 |
DHS.rates Calculates Demographic Indicators | 0.9.2 | 0.9.2 |
diagis Diagnostic Plot and Multivariate Summary Statistics of Weighted Samples from Importance Sampling | 0.2.3 | 0.2.3 |
diagmeta Meta-Analysis of Diagnostic Accuracy Studies with Several Cutpoints | 0.5-0 | 0.5-0 |
DiagnosisMed Diagnostic test accuracy evaluation for medical professionals. | 0.2.3 | 0.2.3 |
diagonals Block Diagonal Extraction or Replacement | 6.4.0 | 6.4.0 |
diagram Functions for Visualising Simple Graphs (Networks), Plotting Flow Diagrams | 1.6.5 | 1.6.5 |
DiagrammeR Graph/Network Visualization | 1.0.10 | 1.0.10 |
DiagrammeRsvg Export DiagrammeR Graphviz Graphs as SVG | 0.1 | 0.1 |
dials Tools for Creating Tuning Parameter Values | 1.2.0 | 1.2.0 |
DiceDesign Designs of Computer Experiments | 1.10 | 1.10 |
DiceEval Construction and Evaluation of Metamodels | 1.6.1 | 1.6.1 |
DiceKriging Kriging Methods for Computer Experiments | 1.6.0 | 1.6.0 |
DiceOptim Kriging-Based Optimization for Computer Experiments | 2.1.1 | 2.1.1 |
dichromat Color Schemes for Dichromats | 2.0-0.1 | 2.0-0.1 |
DICOMread Reading and Saving DICOM Image Files | 0.0.0.3 | 0.0.0.3 |
dictionar6 R6 Dictionary Interface | 0.1.3 | 0.1.3 |
did Treatment Effects with Multiple Periods and Groups | 2.1.1 | 2.1.1 |
dielectric Defines some physical constants and dielectric functions<U+000a>commonly used in optics, plasmonics. | 0.2.3 | 0.2.3 |
DIFboost Detection of Differential Item Functioning (DIF) in Rasch Models by Boosting Techniques | 0.3 | 0.3 |
diffeqr Solving Differential Equations (ODEs, SDEs, DDEs, DAEs) | 2.0.0 | 2.0.0 |
diffobj Diffs for R Objects | 0.3.5 | 0.3.5 |
diffpriv Easy Differential Privacy | 0.4.2 | 0.4.2 |
diffusion Forecast the Diffusion of New Products | 0.2.7 | 0.2.7 |
DIFlasso A Penalty Approach to Differential Item Functioning in Rasch Models | 1.0-4 | 1.0-4 |
difNLR DIF and DDF Detection by Non-Linear Regression Models | 1.4.2-1 | 1.4.2-1 |
DIFplus Multilevel Mantel-Haenszel Statistics for Differential Item Functioning Detection | 1.1 | 1.1 |
difR Collection of Methods to Detect Dichotomous Differential Item Functioning (DIF) | 5.1 | 5.1 |
DIFtree Item Focussed Trees for the Identification of Items in Differential Item Functioning | 3.1.6 | 3.1.6 |
digest Create Compact Hash Digests of R Objects | 0.6.34 | 0.6.34 |
digitize Use Data from Published Plots in R | 0.0.4 | 0.0.4 |
dimensionsR Gathering Bibliographic Records from 'Digital Science Dimensions' Using 'DSL' API | 0.0.3 | 0.0.3 |
dimRed A Framework for Dimensionality Reduction | 0.2.6 | 0.2.6 |
dina Bayesian Estimation of DINA Model | 2.0.0 | 2.0.0 |
dipm Depth Importance in Precision Medicine (DIPM) Method | 1.9 | 1.9 |
DiPs Directional Penalties for Optimal Matching in Observational Studies | 0.6.4 | 0.6.4 |
dipsaus A Dipping Sauce for Data Analysis and Visualizations | 0.2.8 | 0.2.8 |
diptest Hartigan's Dip Test Statistic for Unimodality - Corrected | 0.77-0 | 0.77-0 |
Dire Linear Regressions with a Latent Outcome Variable | 2.2.0 | 2.2.0 |
DIRECT Bayesian Clustering of Multivariate Data Under the Dirichlet-Process Prior | 1.1.0 | 1.1.0 |
DirectEffects Estimating Controlled Direct Effects for Explaining Causal Findings | 0.2.1 | 0.2.1 |
Directional A Collection of Functions for Directional Data Analysis | 6.4 | 6.4 |
directlabels Direct Labels for Multicolor Plots | 2024.1.21 | 2024.1.21 |
dirichletprocess Build Dirichlet Process Objects for Bayesian Modelling | 0.4.2 | 0.4.2 |
dirmult Estimation in Dirichlet-Multinomial Distribution | 0.1.3-5 | 0.1.3-5 |
disaggR Two-Steps Benchmarks for Time Series Disaggregation | 1.0.5.1 | 1.0.5.1 |
discgolf Discourse API Client | 0.2.0 | 0.2.0 |
disclap Discrete Laplace Exponential Family | 1.5.1 | 1.5.1 |
DiscreteInverseWeibull Discrete Inverse Weibull Distribution | 1.0.2 | 1.0.2 |
DiscreteLaplace Discrete Laplace Distributions | 1.1.1 | 1.1.1 |
DiscreteWeibull Discrete Weibull Distributions (Type 1 and 3) | 1.1 | 1.1 |
discretization Data Preprocessing, Discretization for Classification | 1.0-1.1 | 1.0-1.1 |
DiscriMiner Tools of the Trade for Discriminant Analysis | 0.1-29 | 0.1-29 |
discSurv Discrete Time Survival Analysis | 2.0.0 | 2.0.0 |
disk.frame Larger-than-RAM Disk-Based Data Manipulation Framework | 0.8.3 | 0.8.3 |
dismo Species Distribution Modeling | 1.3-14 | 1.3-14 |
disordR Non-Ordered Vectors | 0.9-8.2 | 0.9-8.2 |
Distance Distance Sampling Detection Function and Abundance Estimation | 1.0.9 | 1.0.9 |
distances Tools for Distance Metrics | 0.1.10 | 0.1.10 |
DistatisR DiSTATIS Three Way Metric Multidimensional Scaling | 1.0.1 | 1.0.1 |
distcrete Discrete Distribution Approximations | 1.0.3 | 1.0.3 |
distfree.cr Distribution-Free Confidence Region | 1.5.1 | 1.5.1 |
distill 'R Markdown' Format for Scientific and Technical Writing | 1.6 | 1.6 |
distillery Method Functions for Confidence Intervals and to Distill Information from an Object | 1.2-1 | 1.2-1 |
distory Distance Between Phylogenetic Histories | 1.4.4 | 1.4.4 |
distr Object Oriented Implementation of Distributions | 2.9.3 | 2.9.3 |
distr6 The Complete R6 Probability Distributions Interface | 1.6.9 | 1.6.9 |
distrDoc Documentation for 'distr' Family of R Packages | 2.8.1 | 2.8.1 |
distrEllipse S4 Classes for Elliptically Contoured Distributions | 2.8.2 | 2.8.2 |
distrEx Extensions of Package 'distr' | 2.9.2 | 2.9.2 |
distributional Vectorised Probability Distributions | 0.3.2 | 0.3.2 |
distributions3 Probability Distributions as S3 Objects | 0.2.1 | 0.2.1 |
distributionsrd Distribution Fitting and Evaluation | 0.0.6 | 0.0.6 |
DistributionUtils Distribution Utilities | 0.6-1 | 0.6-1 |
distrMod Object Oriented Implementation of Probability Models | 2.9.0 | 2.9.0 |
distrom Distributed Multinomial Regression | 1.0.1 | 1.0.1 |
distrSim Simulation Classes Based on Package 'distr' | 2.8.2 | 2.8.2 |
distrTeach Extensions of Package 'distr' for Teaching Stochastics/Statistics in Secondary School | 2.9.1 | 2.9.1 |
distrTEst Estimation and Testing Classes Based on Package 'distr' | 2.8.2 | 2.8.2 |
distTails A Collection of Full Defined Distribution Tails | 0.1.2 | 0.1.2 |
dittodb A Test Environment for Database Requests | 0.1.7 | 0.1.7 |
diveMove Dive Analysis and Calibration | 1.6.2 | 1.6.2 |
divest Get Images Out of DICOM Format Quickly | 0.10.3 | 0.10.3 |
divseg Calculate Diversity and Segregation Indices | 0.0.5 | 0.0.5 |
dLagM Time Series Regression Models with Distributed Lag Models | 1.1.13 | 1.1.13 |
dlm Bayesian and Likelihood Analysis of Dynamic Linear Models | 1.1-6 | 1.1-6 |
dlnm Distributed Lag Non-Linear Models | 2.4.7 | 2.4.7 |
dlookr Tools for Data Diagnosis, Exploration, Transformation | 0.6.2 | 0.6.2 |
dlstats Download Stats of R Packages | 0.1.7 | 0.1.7 |
dMod Dynamic Modeling and Parameter Estimation in ODE Models | 1.0.2 | 1.0.2 |
dmri.tracking DiST - Diffusion Direction Smoothing and Tracking | 0.1.0 | 0.1.0 |
dng Distributions and Gradients | 0.2.1 | 0.2.1 |
doBy Groupwise Statistics, LSmeans, Linear Estimates, Utilities | 4.6.20 | 4.6.20 |
docopt Command-Line Interface Specification Language | 0.7.1 | 0.7.1 |
docopulae Optimal Designs for Copula Models | 0.4.0 | 0.4.0 |
dodgr Distances on Directed Graphs | 0.2.14 | 0.2.14 |
DoE.base Full Factorials, Orthogonal Arrays and Base Utilities for DoE Packages | 1.2-4 | 1.2-4 |
DoE.MIParray Creation of Arrays by Mixed Integer Programming | 1.0-1 | 1.0-1 |
DoE.wrapper Wrapper Package for Design of Experiments Functionality | 0.12 | 0.12 |
doFuture Use Foreach to Parallelize via the Future Framework | 1.0.1 | 1.0.1 |
doMC Foreach Parallel Adaptor for 'parallel' | 1.3.8 | 1.3.8 |
domino R Console Bindings for the 'Domino Command-Line Client' | 0.3.1 | 0.3.1 |
doMPI Foreach Parallel Adaptor for the Rmpi Package | 0.2.2 | 0.2.2 |
doParallel Foreach Parallel Adaptor for the 'parallel' Package | 1.0.17 | 1.0.17 |
doRNG Generic Reproducible Parallel Backend for 'foreach' Loops | 1.8.6 | 1.8.6 |
DOSE | 3.30.5 | 3.30.5 |
dosearch Causal Effect Identification from Multiple Incomplete Data Sources | 1.0.11 | 1.0.11 |
DoseFinding Planning and Analyzing Dose Finding Experiments | 1.1-1 | 1.1-1 |
doSNOW Foreach Parallel Adaptor for the 'snow' Package | 1.0.20 | 1.0.20 |
DOSPortfolio Dynamic Optimal Shrinkage Portfolio | 0.1.0 | 0.1.0 |
dosresmeta Multivariate Dose-Response Meta-Analysis | 2.0.1 | 2.0.1 |
dotCall64 Enhanced Foreign Function Interface Supporting Long Vectors | 1.1-1 | 1.1-1 |
dotwhisker Dot-and-Whisker Plots of Regression Results | 0.7.4 | 0.7.4 |
DoubleML Double Machine Learning in R | 0.5.3 | 0.5.3 |
Dowd Functions Ported from 'MMR2' Toolbox Offered in Kevin Dowd's Book Measuring Market Risk | 0.12 | 0.12 |
downlit Syntax Highlighting and Automatic Linking | 0.4.3 | 0.4.3 |
downloader Download Files over HTTP and HTTPS | 0.4 | 0.4 |
dparser Port of 'Dparser' Package | 1.3.1-11 | 1.3.1-11 |
dplyr A Grammar of Data Manipulation | 1.1.4 | 1.1.4 |
DPQ Density, Probability, Quantile ('DPQ') Computations | 0.5-8 | 0.5-8 |
dqrng Fast Pseudo Random Number Generators | 0.3.2 | 0.3.2 |
dr Methods for Dimension Reduction for Regression | 3.0.10 | 3.0.10 |
dr4pl Dose Response Data Analysis using the 4 Parameter Logistic (4pl) Model | 2.0.0 | 2.0.0 |
drake A Pipeline Toolkit for Reproducible Computation at Scale | 7.13.10 | 7.13.10 |
drc Analysis of Dose-Response Curves | 3.0-1 | 3.0-1 |
DRDID Doubly Robust Difference-in-Differences Estimators | 1.0.6 | 1.0.6 |
dreamerr Error Handling Made Easy | 1.4.0 | 1.4.0 |
drgee Doubly Robust Generalized Estimating Equations | 1.1.10 | 1.1.10 |
DriftBurstHypothesis Calculates the Test-Statistic for the Drift Burst Hypothesis | 0.4.0.1 | 0.4.0.1 |
DrImpute Imputing Dropout Events in Single-Cell RNA-Sequencing Data | 1.0 | 1.0 |
DRR Dimensionality Reduction via Regression | 0.0.4 | 0.0.4 |
drtmle Doubly-Robust Nonparametric Estimation and Inference | 1.1.1 | 1.1.1 |
dsa Seasonal Adjustment of Daily Time Series | 1.0.12 | 1.0.12 |
DSAIDE Dynamical Systems Approach to Infectious Disease Epidemiology (Ecology/Evolution) | 0.9.6 | 0.9.6 |
dse Dynamic Systems Estimation (Time Series Package) | 2020.2-1 | 2020.2-1 |
DSI 'DataSHIELD' Interface | 1.5.0 | 1.5.0 |
DSL Distributed Storage and List | 0.1-7 | 0.1-7 |
dslabs Data Science Labs | 0.7.4 | 0.7.4 |
dsm Density Surface Modelling of Distance Sampling Data | 2.3.3 | 2.3.3 |
dstat Conditional Sensitivity Analysis for Matched Observational Studies | 1.0.4 | 1.0.4 |
DT A Wrapper of the JavaScript Library 'DataTables' | 0.31 | 0.31 |
DTAplots Creates Plots Accompanying Bayesian Diagnostic Test Accuracy Meta-Analyses | 1.0.2.5 | 1.0.2.5 |
DTAT Dose Titration Algorithm Tuning | 0.3-6 | 0.3-6 |
DtD Distance to Default | 0.2.2 | 0.2.2 |
DTDA Doubly Truncated Data Analysis | 3.0.1 | 3.0.1 |
dti Analysis of Diffusion Weighted Imaging (DWI) Data | 1.5.4 | 1.5.4 |
dtplyr Data Table Back-End for 'dplyr' | 1.3.1 | 1.3.1 |
DTRlearn2 Statistical Learning Methods for Optimizing Dynamic Treatment Regimes | 1.1 | 1.1 |
DTRreg DTR Estimation and Inference via G-Estimation, Dynamic WOLS, Q-Learning, and Dynamic Weighted Survival Modeling (DWSurv) | 2.0 | 2.0 |
DTSg A Class for Working with Time Series Data Based on 'data.table' and 'R6' with Largely Optional Reference Semantics | 1.1.3 | 1.1.3 |
dtts 'data.table' Time-Series | 0.1.2 | 0.1.2 |
dtw Dynamic Time Warping Algorithms | 1.23-1 | 1.23-1 |
DTWBI Imputation of Time Series Based on Dynamic Time Warping | 1.1 | 1.1 |
dtwclust Time Series Clustering Along with Optimizations for the Dynamic Time Warping Distance | 5.5.12 | 5.5.12 |
DTWUMI Imputation of Multivariate Time Series Based on Dynamic Time Warping | 1.0 | 1.0 |
dual Automatic Differentiation with Dual Numbers | 0.0.5 | 0.0.5 |
duckduckr Simple Client for the DuckDuckGo Instant Answer API | 1.0.0 | 1.0.0 |
dvmisc Convenience Functions, Moving Window Statistics, and Graphics | 1.1.4 | 1.1.4 |
dygraphs Interface to 'Dygraphs' Interactive Time Series Charting Library | 1.1.1.6 | 1.1.1.6 |
Dykstra Quadratic Programming using Cyclic Projections | 1.0-0 | 1.0-0 |
dyn Time Series Regression | 0.2-9.6 | 0.2-9.6 |
dynamicTreeCut Methods for Detection of Clusters in Hierarchical Clustering Dendrograms | 1.63-1 | 1.63-1 |
dynaTree Dynamic Trees for Learning and Design | 1.2-16 | 1.2-16 |
dynlm Dynamic Linear Regression | 0.3-6 | 0.3-6 |
dynpred Companion Package to "Dynamic Prediction in Clinical Survival Analysis" | 0.1.2 | 0.1.2 |
dynsurv Dynamic Models for Survival Data | 0.4-6 | 0.4-6 |
DynTxRegime Methods for Estimating Optimal Dynamic Treatment Regimes | 4.15 | 4.15 |
e1071 Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien | 1.7-14 | 1.7-14 |
eaf Plots of the Empirical Attainment Function | 2.3 | 2.3 |
earlyR Estimation of Transmissibility in the Early Stages of a Disease Outbreak | 0.0.5 | 0.0.5 |
earlywarnings Early Warning Signals for Critical Transitions in Time Series | 1.0.59 | 1.0.59 |
earth Multivariate Adaptive Regression Splines | 5.3.2 | 5.3.2 |
easypower Sample Size Estimation for Experimental Designs | 1.0.1 | 1.0.1 |
easySdcTable Easy Interface to the Statistical Disclosure Control Package 'sdcTable' Extended with Own Implementation of 'GaussSuppression' | 1.0.3 | 1.0.3 |
easystats Framework for Easy Statistical Modeling, Visualization, and Reporting | 0.7.3 | 0.7.3 |
eba Elimination-by-Aspects Models | 1.10-0 | 1.10-0 |
ebal Entropy Reweighting to Create Balanced Samples | 0.1-8 | 0.1-8 |
EbayesThresh Empirical Bayes Thresholding and Related Methods | 1.4-12 | 1.4-12 |
ebdbNet Empirical Bayes Estimation of Dynamic Bayesian Networks | 1.2.8 | 1.2.8 |
EBMAforecast Estimate Ensemble Bayesian Model Averaging Forecasts using Gibbs Sampling or EM-Algorithms | 1.0.31 | 1.0.31 |
ecd Elliptic Lambda Distribution and Option Pricing Model | 0.9.2.4 | 0.9.2.4 |
Ecdat Data Sets for Econometrics | 0.4-0 | 0.4-0 |
ecespa Functions for Spatial Point Pattern Analysis | 1.1-17 | 1.1-17 |
Ecfun Functions for 'Ecdat' | 0.3-1 | 0.3-1 |
eChem Simulations for Electrochemistry Experiments | 1.0.0 | 1.0.0 |
echor Access EPA 'ECHO' Data | 0.1.9 | 0.1.9 |
ECLRMC Ensemble Correlation-Based Low-Rank Matrix Completion | 1.0 | 1.0 |
ecm Build Error Correction Models | 7.0.0 | 7.0.0 |
eco Ecological Inference in 2x2 Tables | 4.0-1 | 4.0-1 |
ecodist Dissimilarity-Based Functions for Ecological Analysis | 2.1.3 | 2.1.3 |
Ecohydmod Ecohydrological Modelling | 1.0.0 | 1.0.0 |
EcoHydRology A Community Modeling Foundation for Eco-Hydrology | 0.4.12.1 | 0.4.12.1 |
ecolMod "A Practical Guide to Ecological Modelling - Using R as a Simulation Platform" | 1.2.6.4 | 1.2.6.4 |
ecoreg Ecological Regression using Aggregate and Individual Data | 0.2.4 | 0.2.4 |
ECOSolveR Embedded Conic Solver in R | 0.5.5 | 0.5.5 |
ecoval Procedures for Ecological Assessment of Surface Waters | 1.2.9 | 1.2.9 |
ecp Non-Parametric Multiple Change-Point Analysis of Multivariate Data | 3.1.5 | 3.1.5 |
ecr Evolutionary Computation in R | 2.1.1 | 2.1.1 |
Ecume Equality of 2 (or k) Continuous Univariate and Multivariate Distributions | 0.9.1 | 0.9.1 |
edfReader Reading EDF(+) and BDF(+) Files | 1.2.1 | 1.2.1 |
edgeR | 4.2.2 | 4.2.2 |
edina Bayesian Estimation of an Exploratory Deterministic Input, Noisy and Gate Model | 0.1.1 | 0.1.1 |
editrules Parsing, Applying, and Manipulating Data Cleaning Rules | 2.9.3 | 2.9.3 |
edmcr Euclidean Distance Matrix Completion Tools | 0.2.0 | 0.2.0 |
edmdata Data Sets for Psychometric Modeling | 1.2.0 | 1.2.0 |
edstan Stan Models for Item Response Theory | 1.0.6 | 1.0.6 |
EdSurvey Analysis of NCES Education Survey and Assessment Data | 4.0.4 | 4.0.4 |
eechidna Exploring Election and Census Highly Informative Data Nationally for Australia | 1.4.1 | 1.4.1 |
eefAnalytics Robust Analytical Methods for Evaluating Educational Interventions using Randomised Controlled Trials Designs | 1.1.1 | 1.1.1 |
eegkit Toolkit for Electroencephalography Data | 1.0-4 | 1.0-4 |
eegkitdata Electroencephalography Toolkit Datasets | 1.1 | 1.1 |
EEM Read and Preprocess Fluorescence Excitation-Emission Matrix (EEM) Data | 1.1.1 | 1.1.1 |
EFAutilities Utility Functions for Exploratory Factor Analysis | 2.1.3 | 2.1.3 |
EffectLiteR Average and Conditional Effects | 0.4-6 | 0.4-6 |
effects Effect Displays for Linear, Generalized Linear, and Other Models | 4.2-1 | 4.2-1 |
effectsize Indices of Effect Size | 0.8.6 | 0.8.6 |
EffectTreat Prediction of Therapeutic Success | 1.1 | 1.1 |
effsize Efficient Effect Size Computation | 0.8.1 | 0.8.1 |
EGAnet Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics | 2.0.3 | 2.0.3 |
egcm Engle-Granger Cointegration Models | 1.0.13 | 1.0.13 |
egg Extensions for 'ggplot2': Custom Geom, Custom Themes, Plot Alignment, Labelled Panels, Symmetric Scales, and Fixed Panel Size | 0.4.5 | 0.4.5 |
egor Import and Analyse Ego-Centered Network Data | 1.23.3 | 1.23.3 |
EGRET Exploration and Graphics for RivEr Trends | 3.0.9 | 3.0.9 |
EGRETci Exploration and Graphics for RivEr Trends Confidence Intervals | 2.0.4 | 2.0.4 |
eha Event History Analysis | 2.11.2 | 2.11.2 |
ei Ecological Inference | 1.3-3 | 1.3-3 |
eicm Explicit Interaction Community Models | 1.0.3 | 1.0.3 |
eigeninv Generates (dense) matrices that have a given set of eigenvalues | 2011.8-1 | 2011.8-1 |
eigenmodel Semiparametric Factor and Regression Models for Symmetric Relational Data | 1.11 | 1.11 |
EigenR Complex Matrix Algebra with 'Eigen' | 1.2.3 | 1.2.3 |
eiPack Ecological Inference and Higher-Dimension Data Management | 0.2-1 | 0.2-1 |
elasdics Elastic Analysis of Sparse, Dense and Irregular Curves | 1.1.3 | 1.1.3 |
elastic General Purpose Interface to 'Elasticsearch' | 1.2.0 | 1.2.0 |
elasticIsing Ising Network Estimation using Elastic Net and k-Fold Cross-Validation | 0.2 | 0.2 |
elasticnet Elastic-Net for Sparse Estimation and Sparse PCA | 1.3 | 1.3 |
elevatr Access Elevation Data from Various APIs | 0.99.0 | 0.99.0 |
elfDistr Kumaraswamy Complementary Weibull Geometric (Kw-CWG) Probability Distribution | 1.0.0 | 1.0.0 |
ellipse Functions for Drawing Ellipses and Ellipse-Like Confidence Regions | 0.5.0 | 0.5.0 |
ellipsis Tools for Working with ... | 0.3.2 | 0.3.2 |
elliptic Weierstrass and Jacobi Elliptic Functions | 1.4-0 | 1.4-0 |
elmNN Implementation of ELM (Extreme Learning Machine ) algorithm for<U+000a>SLFN ( Single Hidden Layer Feedforward Neural Networks ) | 1.0 | 1.0 |
elo Ranking Teams by Elo Rating and Comparable Methods | 3.0.2 | 3.0.2 |
EloChoice Preference Rating for Visual Stimuli Based on Elo Ratings | 0.29.4 | 0.29.4 |
EloOptimized Optimized Elo Rating Method for Obtaining Dominance Ranks | 0.3.1 | 0.3.1 |
EloRating Animal Dominance Hierarchies by Elo Rating | 0.46.11 | 0.46.11 |
ELYP Empirical Likelihood Analysis for the Cox Model and Yang-Prentice (2005) Model | 0.7-5 | 0.7-5 |
emayili Send Email Messages | 0.7.18 | 0.7.18 |
EMbC Expectation-Maximization Binary Clustering | 2.0.4 | 2.0.4 |
EMCluster EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution | 0.2-15 | 0.2-15 |
EMD Empirical Mode Decomposition and Hilbert Spectral Analysis | 1.5.9 | 1.5.9 |
emdbook Support Functions and Data for "Ecological Models and Data" | 1.3.13 | 1.3.13 |
emdi Estimating and Mapping Disaggregated Indicators | 2.2.1 | 2.2.1 |
emg Exponentially Modified Gaussian (EMG) Distribution | 1.0.9 | 1.0.9 |
emmeans Estimated Marginal Means, aka Least-Squares Means | 1.10.0 | 1.10.0 |
emoa Evolutionary Multiobjective Optimization Algorithms | 0.5-2 | 0.5-2 |
empichar Evaluates the Empirical Characteristic Function for Multivariate Samples | 1.0.0 | 1.0.0 |
EmpiricalCalibration Routines for Performing Empirical Calibration of Observational Study Estimates | 3.1.2 | 3.1.2 |
emplik Empirical Likelihood Ratio for Censored/Truncated Data | 1.3-1 | 1.3-1 |
emplik2 Empirical Likelihood Ratio Test for Two-Sample U-Statistics with Censored Data | 1.32 | 1.32 |
emulator Bayesian Emulation of Computer Programs | 1.2-21 | 1.2-21 |
endoSwitch Endogenous Switching Regression Models | 1.0.0 | 1.0.0 |
endtoend Transmissions and Receptions in an End to End Network | 2.29 | 2.29 |
energy E-Statistics: Multivariate Inference via the Energy of Data | 1.7-10 | 1.7-10 |
english Translate Integers into English | 1.2-6 | 1.2-6 |
EngrExpt Data sets from "Introductory Statistics for Engineering<U+000a>Experimentation" | 0.1-8 | 0.1-8 |
engsoccerdata English and European Soccer Results 1871-2016 | 0.1.5 | 0.1.5 |
enpls Ensemble Partial Least Squares Regression | 6.1 | 6.1 |
enrichplot | 1.24.4 | 1.24.4 |
enrichR Provides an R Interface to 'Enrichr' | 3.0 | 3.0 |
enrichwith Methods to Enrich R Objects with Extra Components | 0.3.1 | 0.3.1 |
EnsDb.Hsapiens.v86 | 2.99.0 | 2.99.0 |
ensembldb | 2.22.0 | 2.22.0 |
ensembleBMA Probabilistic Forecasting using Ensembles and Bayesian Model Averaging | 5.1.8 | 5.1.8 |
entropy Estimation of Entropy, Mutual Information and Related Quantities | 1.3.1 | 1.3.1 |
EntropyMCMC MCMC Simulation and Convergence Evaluation using Entropy and Kullback-Leibler Divergence Estimation | 1.0.4 | 1.0.4 |
envnames Keep Track of User-Defined Environment Names | 0.4.1 | 0.4.1 |
EnvStats Package for Environmental Statistics, Including US EPA Guidance | 2.8.1 | 2.8.1 |
Epi Statistical Analysis in Epidemiology | 2.47.1 | 2.47.1 |
epibasix Elementary Epidemiological Functions for Epidemiology and Biostatistics | 1.5 | 1.5 |
epicalc Epidemiological calculator | 2.15.1.0 | 2.15.1.0 |
epicontacts Handling, Visualisation and Analysis of Epidemiological Contacts | 1.1.3 | 1.1.3 |
EpiContactTrace Epidemiological Tool for Contact Tracing | 0.17.0 | 0.17.0 |
EpiCurve Plot an Epidemic Curve | 2.4-2 | 2.4-2 |
epiDisplay Epidemiological Data Display Package | 3.5.0.2 | 3.5.0.2 |
EpiEstim Estimate Time Varying Reproduction Numbers from Epidemic Curves | 2.2-4 | 2.2-4 |
epiflows Predicting Disease Spread from Flow Data | 0.2.1 | 0.2.1 |
EpiILM Spatial and Network Based Individual Level Models for Epidemics | 1.5.2 | 1.5.2 |
EpiILMCT Continuous Time Distance-Based and Network-Based Individual Level Models for Epidemics | 1.1.7 | 1.1.7 |
epimdr Functions and Data for "Epidemics: Models and Data in R" | 0.6-5 | 0.6-5 |
EpiModel Mathematical Modeling of Infectious Disease Dynamics | 2.4.0 | 2.4.0 |
epinet Epidemic/Network-Related Tools | 2.1.11 | 2.1.11 |
epiR Tools for the Analysis of Epidemiological Data | 2.0.67 | 2.0.67 |
EpiReport Epidemiological Report | 1.0.2 | 1.0.2 |
episensr Basic Sensitivity Analysis of Epidemiological Results | 1.1.0 | 1.1.0 |
epitools Epidemiology Tools | 0.5-10.1 | 0.5-10.1 |
epitrix Small Helpers and Tricks for Epidemics Analysis | 0.4.0 | 0.4.0 |
equate Observed-Score Linking and Equating | 2.0.8 | 2.0.8 |
equateIRT IRT Equating Methods | 2.3.0 | 2.3.0 |
equateMultiple Equating of Multiple Forms | 0.1.1 | 0.1.1 |
equivalence Provides Tests and Graphics for Assessing Tests of Equivalence | 0.7.2 | 0.7.2 |
ercv Fitting Tails by the Empirical Residual Coefficient of Variation | 1.0.1 | 1.0.1 |
erer Empirical Research in Economics with R | 3.1 | 3.1 |
ergm Fit, Simulate and Diagnose Exponential-Family Models for Networks | 4.6.0 | 4.6.0 |
ergm.count Fit, Simulate and Diagnose Exponential-Family Models for Networks with Count Edges | 4.1.1 | 4.1.1 |
ergm.ego Fit, Simulate and Diagnose Exponential-Family Random Graph Models to Egocentrically Sampled Network Data | 1.1.0 | 1.1.0 |
ergm.multi Fit, Simulate and Diagnose Exponential-Family Models for Multiple or Multilayer Networks | 0.2.0 | 0.2.0 |
eRm Extended Rasch Modeling | 1.0-4 | 1.0-4 |
errorlocate Locate Errors with Validation Rules | 1.1.1 | 1.1.1 |
errors Uncertainty Propagation for R Vectors | 0.4.1 | 0.4.1 |
errum Exploratory Reduced Reparameterized Unified Model Estimation | 0.0.3 | 0.0.3 |
es.dif Compute Effect Sizes of the Difference | 1.0.2 | 1.0.2 |
esaBcv Estimate Number of Latent Factors and Factor Matrix for Factor Analysis | 1.2.1.1 | 1.2.1.1 |
esc Effect Size Computation for Meta Analysis | 0.5.1 | 0.5.1 |
esemifar Smoothing Long-Memory Time Series | 1.0.2 | 1.0.2 |
ESG A Package for Asset Projection | 1.3 | 1.3 |
EstCRM Calibrating Parameters for the Samejima's Continuous IRT Model | 1.6 | 1.6 |
estimability Tools for Assessing Estimability of Linear Predictions | 1.4.1 | 1.4.1 |
EstimateGroupNetwork Perform the Joint Graphical Lasso and Selects Tuning Parameters | 0.3.1 | 0.3.1 |
estimatr Fast Estimators for Design-Based Inference | 1.0.2 | 1.0.2 |
estimraw Estimation of Four-Fold Table Cell Frequencies (Raw Data) from Effect Size Measures | 1.0.0 | 1.0.0 |
estmeansd Estimating the Sample Mean and Standard Deviation from Commonly Reported Quantiles in Meta-Analysis | 1.0.1 | 1.0.1 |
estudy2 An Implementation of Parametric and Nonparametric Event Study | 0.10.0 | 0.10.0 |
etm Empirical Transition Matrix | 1.1.1 | 1.1.1 |
etma Epistasis Test in Meta-Analysis | 1.1-1 | 1.1-1 |
etrm Energy Trading and Risk Management | 1.0.1 | 1.0.1 |
etrunct Computes Moments of Univariate Truncated t Distribution | 0.1 | 0.1 |
EUfootball Football Match Data of European Leagues | 0.0.1 | 0.0.1 |
europepmc R Interface to the Europe PubMed Central RESTful Web Service | 0.4.3 | 0.4.3 |
eurostat Tools for Eurostat Open Data | 4.0.0 | 4.0.0 |
eva Extreme Value Analysis with Goodness-of-Fit Testing | 0.2.6 | 0.2.6 |
evalITR Evaluating Individualized Treatment Rules | 1.0.0 | 1.0.0 |
evaluate Parsing and Evaluation Tools that Provide More Details than the Default | 0.23 | 0.23 |
EValue Sensitivity Analyses for Unmeasured Confounding and Other Biases in Observational Studies and Meta-Analyses | 4.1.3 | 4.1.3 |
Evapotranspiration Modelling Actual, Potential and Reference Crop Evapotranspiration | 1.16 | 1.16 |
evclass Evidential Distance-Based Classification | 2.0.2 | 2.0.2 |
evclust Evidential Clustering | 2.0.3 | 2.0.3 |
evd Functions for Extreme Value Distributions | 2.3-6.1 | 2.3-6.1 |
evgam Generalised Additive Extreme Value Models | 1.0.0 | 1.0.0 |
EvidenceSynthesis Synthesizing Causal Evidence in a Distributed Research Network | 0.5.0 | 0.5.0 |
evir Extreme Values in R | 1.7-4 | 1.7-4 |
evmix Extreme Value Mixture Modelling, Threshold Estimation and Boundary Corrected Kernel Density Estimation | 2.12 | 2.12 |
evtree Evolutionary Learning of Globally Optimal Trees | 1.0-8 | 1.0-8 |
ewoc Escalation with Overdose Control | 0.3.0 | 0.3.0 |
Exact Unconditional Exact Test | 3.2 | 3.2 |
exactci Exact P-Values and Matching Confidence Intervals for Simple Discrete Parametric Cases | 1.4-4 | 1.4-4 |
exactextractr Fast Extraction from Raster Datasets using Polygons | 0.10.0 | 0.10.0 |
exactLoglinTest Monte Carlo Exact Tests for Log-linear models | 1.4.2 | 1.4.2 |
exactmeta Exact fixed effect meta analysis | 1.0-2 | 1.0-2 |
exactRankTests Exact Distributions for Rank and Permutation Tests | 0.8-35 | 0.8-35 |
exams Automatic Generation of Exams in R | 2.4-0 | 2.4-0 |
ExceedanceTools Confidence/Credible Regions for Exceedance Sets and Contour Lines | 1.3.6 | 1.3.6 |
exdex Estimation of the Extremal Index | 1.2.3 | 1.2.3 |
experiment R Package for Designing and Analyzing Randomized Experiments | 1.2.1 | 1.2.1 |
expint Exponential Integral and Incomplete Gamma Function | 0.1-8 | 0.1-8 |
expm Matrix Exponential, Log, 'etc' | 0.999-9 | 0.999-9 |
export Streamlined Export of Graphs and Data Tables | 0.3.0 | 0.3.0 |
ExPosition Exploratory Analysis with the Singular Value Decomposition | 2.8.23 | 2.8.23 |
expsmooth Data Sets from "Forecasting with Exponential Smoothing" | 2.3 | 2.3 |
exreport Fast, Reliable and Elegant Reproducible Research | 0.4.1 | 0.4.1 |
extraDistr Additional Univariate and Multivariate Distributions | 1.10.0 | 1.10.0 |
extrafont Tools for Using Fonts | 0.19 | 0.19 |
extrafontdb Package for holding the database for the extrafont package | 1.0 | 1.0 |
extraoperators Extra Binary Relational and Logical Operators | 0.3.0 | 0.3.0 |
extras Helper Functions for Bayesian Analyses | 0.6.1 | 0.6.1 |
extraTrees Extremely Randomized Trees (ExtraTrees) Method for<U+000a>Classification and Regression | 1.0.5 | 1.0.5 |
ExtremalDep Extremal Dependence Models | 0.0.4-1 | 0.0.4-1 |
ExtremeBounds Extreme Bounds Analysis (EBA) | 0.1.7 | 0.1.7 |
extremefit Estimation of Extreme Conditional Quantiles and Probabilities | 1.0.2 | 1.0.2 |
ExtremeRisks Extreme Risk Measures | 0.0.4 | 0.0.4 |
extRemes Extreme Value Analysis | 2.1-3 | 2.1-3 |
extremeStat Extreme Value Statistics and Quantile Estimation | 1.5.9 | 1.5.9 |
extremevalues Univariate Outlier Detection | 2.3.3 | 2.3.3 |
extremis Statistics of Extremes | 1.2.1 | 1.2.1 |
exuber Econometric Analysis of Explosive Time Series | 0.4.2 | 0.4.2 |
eyelinker Import ASC Files from EyeLink Eye Trackers | 0.2.1 | 0.2.1 |
ez Easy Analysis and Visualization of Factorial Experiments | 4.4-0 | 4.4-0 |
fable Forecasting Models for Tidy Time Series | 0.3.3 | 0.3.3 |
fable.prophet Prophet Modelling Interface for 'fable' | 0.1.0 | 0.1.0 |
fabletools Core Tools for Packages in the 'fable' Framework | 0.3.4 | 0.3.4 |
fabricatr Imagine Your Data Before You Collect It | 1.0.0 | 1.0.0 |
face Fast Covariance Estimation for Sparse Functional Data | 0.1-6 | 0.1-6 |
FactoClass Combination of Factorial Methods and Cluster Analysis | 1.2.8 | 1.2.8 |
factoextra Extract and Visualize the Results of Multivariate Data Analyses | 1.0.7 | 1.0.7 |
FactoMineR Multivariate Exploratory Data Analysis and Data Mining | 2.11 | 2.11 |
factorstochvol Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models | 1.1.0 | 1.1.0 |
FAdist Distributions that are Sometimes Used in Hydrology | 2.4 | 2.4 |
fail File Abstraction Interface Layer (FAIL) | 1.3 | 1.3 |
fairml Fair Models in Machine Learning | 0.6.3 | 0.6.3 |
fairmodels Flexible Tool for Bias Detection, Visualization, and Mitigation | 1.2.0 | 1.2.0 |
fame Interface for FAME Time Series Database | 2.21.1 | 2.21.1 |
FamEvent Family Age-at-Onset Data Simulation and Penetrance Estimation | 3.0 | 3.0 |
fANCOVA Nonparametric Analysis of Covariance | 0.6-1 | 0.6-1 |
fanplot Visualisation of Sequential Probability Distributions Using Fan Charts | 4.0.0 | 4.0.0 |
fansi ANSI Control Sequence Aware String Functions | 1.0.6 | 1.0.6 |
FAOSTAT Download Data from the FAOSTAT Database | 2.3.0 | 2.3.0 |
faoutlier Influential Case Detection Methods for Factor Analysis and Structural Equation Models | 0.7.6 | 0.7.6 |
faraway Functions and Datasets for Books by Julian Faraway | 1.0.8 | 1.0.8 |
farver High Performance Colour Space Manipulation | 2.1.1 | 2.1.1 |
fAsianOptions Rmetrics - EBM and Asian Option Valuation | 3042.82 | 3042.82 |
fAssets Rmetrics - Analysing and Modelling Financial Assets | 4023.85 | 4023.85 |
fasstr Analyze, Summarize, and Visualize Daily Streamflow Data | 0.5.1 | 0.5.1 |
fastcluster Fast Hierarchical Clustering Routines for R and 'Python' | 1.2.6 | 1.2.6 |
fastcox Lasso and Elastic-Net Penalized Cox's Regression in High Dimensions Models using the Cocktail Algorithm | 1.1.3 | 1.1.3 |
fastDummies Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables | 1.7.3 | 1.7.3 |
fastGHQuad Fast 'Rcpp' Implementation of Gauss-Hermite Quadrature | 1.0.1 | 1.0.1 |
fastglm Fast and Stable Fitting of Generalized Linear Models using 'RcppEigen' | 0.0.3 | 0.0.3 |
fastICA FastICA Algorithms to Perform ICA and Projection Pursuit | 1.2-4 | 1.2-4 |
fastLink Fast Probabilistic Record Linkage with Missing Data | 0.6.0 | 0.6.0 |
fastmap Fast Data Structures | 1.1.1 | 1.1.1 |
fastmatch Fast 'match()' Function | 1.1-4 | 1.1-4 |
fastpseudo Fast Pseudo Observations | 0.1 | 0.1 |
fastRhockey Functions to Access Premier Hockey Federation and National Hockey League Play by Play Data | 0.4.0 | 0.4.0 |
fastrmodels Models for the 'nflfastR' Package | 1.0.2 | 1.0.2 |
FastRWeb Fast Interactive Framework for Web Scripting Using R | 1.2-1 | 1.2-1 |
fastshap Fast Approximate Shapley Values | 0.0.7 | 0.0.7 |
fasttime Fast Utility Function for Time Parsing and Conversion | 1.1-0 | 1.1-0 |
FateID Quantification of Fate Bias in Multipotent Progenitors | 0.2.2 | 0.2.2 |
FatTailsR Kiener Distributions and Fat Tails in Finance | 1.8-0 | 1.8-0 |
fauxpas HTTP Error Helpers | 0.5.2 | 0.5.2 |
FAVAR Bayesian Analysis of a FAVAR Model | 0.1.3 | 0.1.3 |
fBasics Rmetrics - Markets and Basic Statistics | 4032.96 | 4032.96 |
FBFsearch Algorithm for Searching the Space of Gaussian Directed Acyclic Graph Models Through Moment Fractional Bayes Factors | 1.2 | 1.2 |
fBonds Rmetrics - Pricing and Evaluating Bonds | 3042.78 | 3042.78 |
fbRads Analyzing and Managing Facebook Ads from R | 17.0.0 | 17.0.0 |
fbRanks Association Football (Soccer) Ranking via Poisson Regression | 2.0 | 2.0 |
fclust Fuzzy Clustering | 2.1.1.1 | 2.1.1.1 |
fCopulae Rmetrics - Bivariate Dependence Structures with Copulae | 4022.85 | 4022.85 |
FCPS Fundamental Clustering Problems Suite | 1.3.4 | 1.3.4 |
FD Measuring Functional Diversity (FD) from Multiple Traits, and Other Tools for Functional Ecology | 1.0-12.3 | 1.0-12.3 |
fda Functional Data Analysis | 6.1.4 | 6.1.4 |
fda.usc Functional Data Analysis and Utilities for Statistical Computing | 2.1.0 | 2.1.0 |
fdaACF Autocorrelation Function for Functional Time Series | 1.0.0 | 1.0.0 |
fdadensity Functional Data Analysis for Density Functions by Transformation to a Hilbert Space | 0.1.2 | 0.1.2 |
fdakma Functional Data Analysis: K-Mean Alignment | 1.2.1 | 1.2.1 |
fdANOVA Analysis of Variance for Univariate and Multivariate Functional Data | 0.1.2 | 0.1.2 |
fdaoutlier Outlier Detection Tools for Functional Data Analysis | 0.2.1 | 0.2.1 |
fdapace Functional Data Analysis and Empirical Dynamics | 0.5.9 | 0.5.9 |
fdaPDE Physics-Informed Spatial and Functional Data Analysis | 1.1-17 | 1.1-17 |
fdasrvf Elastic Functional Data Analysis | 2.1.2 | 2.1.2 |
fdatest Interval Testing Procedure for Functional Data | 2.1.1 | 2.1.1 |
FDboost Boosting Functional Regression Models | 1.1-2 | 1.1-2 |
fdrtool Estimation of (Local) False Discovery Rates and Higher Criticism | 1.2.17 | 1.2.17 |
fds Functional Data Sets | 1.8 | 1.8 |
feasts Feature Extraction and Statistics for Time Series | 0.3.1 | 0.3.1 |
feather R Bindings to the Feather 'API' | 0.3.5 | 0.3.5 |
features Feature Extraction for Discretely-Sampled Functional Data | 2015.12-1 | 2015.12-1 |
fechner Fechnerian Scaling of Discrete Object Sets | 1.0-3 | 1.0-3 |
FedData Functions to Automate Downloading Geospatial Data Available from Several Federated Data Sources | 4.0.0 | 4.0.0 |
FeedbackTS Analysis of Feedback in Time Series | 1.5 | 1.5 |
feisr Estimating Fixed Effects Individual Slope Models | 1.3.0 | 1.3.0 |
fExoticOptions Rmetrics - Pricing and Evaluating Exotic Option | 3042.80 | 3042.80 |
fExtremes Rmetrics - Modelling Extreme Events in Finance | 4032.84 | 4032.84 |
ff Memory-Efficient Storage of Large Data on Disk and Fast Access Functions | 4.0.12 | 4.0.12 |
FFD Freedom from Disease | 1.0-9 | 1.0-9 |
FFdownload Download Data from Kenneth French's Website | 1.1.0 | 1.1.0 |
FField Force field simulation for a set of points | 0.1.0 | 0.1.0 |
fflr Retrieve ESPN Fantasy Football Data | 2.2.1 | 2.2.1 |
ffscrapr API Client for Fantasy Football League Platforms | 1.4.8 | 1.4.8 |
ffsimulator Simulate Fantasy Football Seasons | 1.2.3 | 1.2.3 |
fftw Fast FFT and DCT Based on the FFTW Library | 1.0-7 | 1.0-7 |
fftwtools Wrapper for 'FFTW3' Includes: One-Dimensional, Two-Dimensional, Three-Dimensional, and Multivariate Transforms | 0.9-11 | 0.9-11 |
fgac Generalized Archimedean Copula | 0.6-1 | 0.6-1 |
fGarch Rmetrics - Autoregressive Conditional Heteroskedastic Modelling | 4031.90 | 4031.90 |
fgsea | 1.30.0 | 1.30.0 |
FHDI Fractional Hot Deck and Fully Efficient Fractional Imputation | 1.4.1 | 1.4.1 |
fields Tools for Spatial Data | 15.2 | 15.2 |
FieldSim Random Fields (and Bridges) Simulations | 3.2.1 | 3.2.1 |
fiery A Lightweight and Flexible Web Framework | 1.2.0 | 1.2.0 |
filearray File-Backed Array for Out-of-Memory Computation | 0.1.6 | 0.1.6 |
filehash Simple Key-Value Database | 2.4-5 | 2.4-5 |
filehashSQLite Simple Key-Value Database using SQLite | 0.2-6 | 0.2-6 |
filelock Portable File Locking | 1.0.3 | 1.0.3 |
filematrix File-Backed Matrix Class with Convenient Read and Write Access | 1.3 | 1.3 |
FILEST Fine-Level Structure Simulator | 1.1.2 | 1.1.2 |
filling Matrix Completion, Imputation, and Inpainting Methods | 0.2.3 | 0.2.3 |
fImport Rmetrics - Importing Economic and Financial Data | 4032.87 | 4032.87 |
FinancialInstrument Financial Instrument Model Infrastructure and Meta-Data | 1.3.1 | 1.3.1 |
FinancialMath Financial Mathematics for Actuaries | 0.1.1 | 0.1.1 |
FinAsym Classifies implicit trading activity from market quotes and<U+000a>computes the probability of informed trading | 1.0 | 1.0 |
FindIt Finding Heterogeneous Treatment Effects | 1.2.0 | 1.2.0 |
findpython Functions to Find an Acceptable Python Binary | 1.0.7 | 1.0.7 |
fingerprint Functions to Operate on Binary Fingerprint Data | 3.5.7 | 3.5.7 |
finreportr Financial Data from U.S. Securities and Exchange Commission | 1.0.4 | 1.0.4 |
FinTS Companion to Tsay (2005) Analysis of Financial Time Series | 0.4-9 | 0.4-9 |
FisherEM The FisherEM Algorithm to Simultaneously Cluster and Visualize High-Dimensional Data | 1.6 | 1.6 |
fishmethods Fishery Science Methods and Models | 1.11-3 | 1.11-3 |
fishmove Prediction of Fish Movement Parameters | 0.3-3 | 0.3-3 |
fit.models Compare Fitted Models | 0.64 | 0.64 |
fitbitScraper Scrapes Data from Fitbit | 0.1.8 | 0.1.8 |
fitcoach Personalized Coach for Fitbit and R API | 1.0 | 1.0 |
fitdistrplus Help to Fit of a Parametric Distribution to Non-Censored or Censored Data | 1.1-11 | 1.1-11 |
FITSio FITS (Flexible Image Transport System) Utilities | 2.1-6 | 2.1-6 |
fitteR Fit Hundreds of Theoretical Distributions to Empirical Data | 0.2.0 | 0.2.0 |
fitzRoy Easily Scrape and Process AFL Data | 1.3.0 | 1.3.0 |
FixedPoint Algorithms for Finding Fixed Point Vectors of Functions | 0.6.3 | 0.6.3 |
fixest Fast Fixed-Effects Estimations | 0.11.2 | 0.11.2 |
FKF Fast Kalman Filter | 0.2.5 | 0.2.5 |
FKF.SP Fast Kalman Filtering Through Sequential Processing | 0.3.1 | 0.3.1 |
flacco Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems | 1.8 | 1.8 |
FLAME Interpretable Matching for Causal Inference | 2.1.1 | 2.1.1 |
flare Family of Lasso Regression | 1.7.0.1 | 1.7.0.1 |
flashClust Implementation of optimal hierarchical clustering | 1.01-2 | 1.01-2 |
flexclust Flexible Cluster Algorithms | 1.4-1 | 1.4-1 |
flexdashboard R Markdown Format for Flexible Dashboards | 0.5.2 | 0.5.2 |
FlexDir Tools to Work with the Flexible Dirichlet Distribution | 1.0 | 1.0 |
flexiblas 'FlexiBLAS' API Interface | 3.4.0 | 3.4.0 |
flexmix Flexible Mixture Modeling | 2.3-19 | 2.3-19 |
flexrsurv Flexible Relative Survival Analysis | 2.0.17 | 2.0.17 |
FlexScan Flexible Scan Statistics | 0.2.2 | 0.2.2 |
flexsurv Flexible Parametric Survival and Multi-State Models | 2.2.2 | 2.2.2 |
flextable Functions for Tabular Reporting | 0.9.4 | 0.9.4 |
float 32-Bit Floats | 0.3-2 | 0.3-2 |
flock Process Synchronization Using File Locks | 0.7 | 0.7 |
flowr Streamlining Design and Deployment of Complex Workflows | 0.9.11 | 0.9.11 |
FlowScreen Daily Streamflow Trend and Change Point Screening | 1.2.6 | 1.2.6 |
flsa Path Algorithm for the General Fused Lasso Signal Approximator | 1.5.4 | 1.5.4 |
FLSSS Mining Rigs for Problems in the Subset Sum Family | 9.1.1 | 9.1.1 |
fma Data Sets from "Forecasting: Methods and Applications" by Makridakis, Wheelwright & Hyndman (1998) | 2.5 | 2.5 |
FMC Factorial Experiments with Minimum Level Changes | 1.0.1 | 1.0.1 |
fmdates Financial Market Date Calculations | 0.1.4 | 0.1.4 |
FME A Flexible Modelling Environment for Inverse Modelling, Sensitivity, Identifiability and Monte Carlo Analysis | 1.3.6.3 | 1.3.6.3 |
fmesher Triangle Meshes and Related Geometry Tools | 0.1.7 | 0.1.7 |
fmri Analysis of fMRI Experiments | 1.9.12 | 1.9.12 |
FMStable Finite Moment Stable Distributions | 0.1-4 | 0.1-4 |
fMultivar Rmetrics - Modeling of Multivariate Financial Return Distributions | 4031.84 | 4031.84 |
FNN Fast Nearest Neighbor Search Algorithms and Applications | 1.1.4 | 1.1.4 |
fNonlinear Rmetrics - Nonlinear and Chaotic Time Series Modelling | 4021.81 | 4021.81 |
foghorn Summarize CRAN Check Results in the Terminal | 1.4.2 | 1.4.2 |
foieGras Fit Continuous-Time State-Space and Latent Variable Models for Quality Control of Argos Satellite (and Other) Telemetry Data and for Estimating Movement Behaviour | 0.7-6 | 0.7-6 |
fontawesome Easily Work with 'Font Awesome' Icons | 0.5.2 | 0.5.2 |
fontBitstreamVera Fonts with 'Bitstream Vera Fonts' License | 0.1.1 | 0.1.1 |
fontLiberation Liberation Fonts | 0.1.0 | 0.1.0 |
fontquiver Set of Installed Fonts | 0.2.1 | 0.2.1 |
footballpenaltiesBL Penalties in the German Men's Football Bundesliga | 1.0.0 | 1.0.0 |
footBayes Fitting Bayesian and MLE Football Models | 0.2.0 | 0.2.0 |
fOptions Rmetrics - Pricing and Evaluating Basic Options | 3042.86 | 3042.86 |
forcats Tools for Working with Categorical Variables (Factors) | 1.0.0 | 1.0.0 |
foreach Provides Foreach Looping Construct | 1.5.2 | 1.5.2 |
ForeCA Forecastable Component Analysis | 0.2.7 | 0.2.7 |
forecast Forecasting Functions for Time Series and Linear Models | 8.21.1 | 8.21.1 |
ForecastComb Forecast Combination Methods | 1.3.1 | 1.3.1 |
forecastHybrid Convenient Functions for Ensemble Time Series Forecasts | 5.0.19 | 5.0.19 |
forecastML Time Series Forecasting with Machine Learning Methods | 0.9.0 | 0.9.0 |
FoReco Forecast Reconciliation | 0.2.6 | 0.2.6 |
forecTheta Forecasting Time Series by Theta Models | 2.6.2 | 2.6.2 |
foreign Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', ... | 0.8-86 | 0.8-86 |
ForestFit Statistical Modelling for Plant Size Distributions | 2.2.3 | 2.2.3 |
forestmodel Forest Plots from Regression Models | 0.6.2 | 0.6.2 |
forestplot Advanced Forest Plot Using 'grid' Graphics | 3.1.3 | 3.1.3 |
forestploter Create a Flexible Forest Plot | 1.1.1 | 1.1.1 |
forge Casting Values into Shape | 0.2.0 | 0.2.0 |
ForImp Imputation of Missing Values Through a Forward Imputation Algorithm | 1.0.3 | 1.0.3 |
formatR Format R Code Automatically | 1.14 | 1.14 |
formattable Create 'Formattable' Data Structures | 0.2.1 | 0.2.1 |
Formula Extended Model Formulas | 1.2-5 | 1.2-5 |
formula.tools Programmatic Utilities for Manipulating Formulas, Expressions, Calls, Assignments and Other R Objects | 1.7.1 | 1.7.1 |
forplo Flexible Forest Plots | 0.2.5 | 0.2.5 |
forward Robust Analysis using Forward Search | 1.0.6 | 1.0.6 |
foster Forest Structure Extrapolation with R | 0.1.1 | 0.1.1 |
fourierin Computes Numeric Fourier Integrals | 0.2.4 | 0.2.4 |
fourPNO Bayesian 4 Parameter Item Response Model | 1.1.0 | 1.1.0 |
fpc Flexible Procedures for Clustering | 2.2-13 | 2.2-13 |
fpcb Predictive Confidence Bands for Functional Time Series Forecasting | 0.1.0 | 0.1.0 |
fpCompare Reliable Comparison of Floating Point Numbers | 0.2.4 | 0.2.4 |
FPLdata Read in Fantasy Premier League Data | 0.1.0 | 0.1.0 |
fPortfolio Rmetrics - Portfolio Selection and Optimization | 4023.84 | 4023.84 |
fpow Computing the noncentrality parameter of the noncentral F distribution | 0.0-2 | 0.0-2 |
fpp2 Data for "Forecasting: Principles and Practice" (2nd Edition) | 2.5 | 2.5 |
fpp3 Data for "Forecasting: Principles and Practice" (3rd Edition) | 0.5 | 0.5 |
frab How to Add Two R Tables | 0.0-6 | 0.0-6 |
fracdiff Fractionally Differenced ARIMA aka ARFIMA(P,d,q) Models | 1.5-2 | 1.5-2 |
frailtyEM Fitting Frailty Models with the EM Algorithm | 1.0.1 | 1.0.1 |
frailtyHL Frailty Models via Hierarchical Likelihood | 2.3 | 2.3 |
frailtypack Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints | 3.5.1 | 3.5.1 |
frailtySurv General Semiparametric Shared Frailty Model | 1.3.8 | 1.3.8 |
Frames2 Estimation in Dual Frame Surveys | 0.2.1 | 0.2.1 |
FRAPO Financial Risk Modelling and Portfolio Optimisation with R | 0.4-1 | 0.4-1 |
frbs Fuzzy Rule-Based Systems for Classification and Regression Tasks | 3.2-0 | 3.2-0 |
frechet Statistical Analysis for Random Objects and Non-Euclidean Data | 0.3.0 | 0.3.0 |
fredr An R Client for the 'FRED' API | 2.1.0 | 2.1.0 |
freealg The Free Algebra | 1.1-1 | 1.1-1 |
freegroup The Free Group | 1.1-8 | 1.1-8 |
freesurferformats Read and Write 'FreeSurfer' Neuroimaging File Formats | 0.1.17 | 0.1.17 |
freetypeharfbuzz Deterministic Computation of Text Box Metrics | 0.2.6 | 0.2.6 |
fRegression Rmetrics - Regression Based Decision and Prediction | 4021.83 | 4021.83 |
frenchdata Download Data Sets from Kenneth's French Finance Data Library Site | 0.2.0 | 0.2.0 |
freqdom Frequency Domain Based Analysis: Dynamic PCA | 2.0.3 | 2.0.3 |
freqdom.fda Functional Time Series: Dynamic Functional Principal Components | 1.0.1 | 1.0.1 |
fresh Create Custom 'Bootstrap' Themes to Use in 'Shiny' | 0.2.0 | 0.2.0 |
FrF2 Fractional Factorial Designs with 2-Level Factors | 2.3-3 | 2.3-3 |
FrF2.catlg128 Catalogues of Resolution IV 128 Run 2-Level Fractional Factorials Up to 33 Factors that Do Have 5-Letter Words | 1.2-3 | 1.2-3 |
FRK Fixed Rank Kriging | 2.2.1 | 2.2.1 |
frm Regression Analysis of Fractional Responses | 1.2.2 | 1.2.2 |
frmqa The Generalized Hyperbolic Distribution, Related Distributions and Their Applications in Finance | 0.1-5 | 0.1-5 |
fromo Fast Robust Moments | 0.2.1 | 0.2.1 |
frontier Stochastic Frontier Analysis | 1.1-8 | 1.1-8 |
fs Cross-Platform File System Operations Based on 'libuv' | 1.6.3 | 1.6.3 |
FSelector Selecting Attributes | 0.34 | 0.34 |
fsMTS Feature Selection for Multivariate Time Series | 0.1.7 | 0.1.7 |
FSMUMI Imputation of Time Series Based on Fuzzy Logic | 1.0 | 1.0 |
fsn Rosenthal's Fail Safe Number and Related Functions | 0.2 | 0.2 |
fso Fuzzy Set Ordination | 2.1-2 | 2.1-2 |
fst Lightning Fast Serialization of Data Frames | 0.9.8 | 0.9.8 |
fstcore R Bindings to the 'Fstlib' Library | 0.9.18 | 0.9.18 |
fTrading Rmetrics - Trading and Rebalancing Financial Instruments | 3042.79 | 3042.79 |
fts R Interface to 'tslib' (a Time Series Library in C++) | 0.9.9.2 | 0.9.9.2 |
ftsa Functional Time Series Analysis | 6.3 | 6.3 |
ftsspec Spectral Density Estimation and Comparison for Functional Time Series | 1.0.0 | 1.0.0 |
functional Curry, Compose, and other higher-order functions | 0.6 | 0.6 |
funData An S4 Class for Functional Data | 1.3-8 | 1.3-8 |
funFEM Clustering in the Discriminative Functional Subspace | 1.2 | 1.2 |
fungible Psychometric Functions from the Waller Lab | 2.4.4 | 2.4.4 |
funHDDC Univariate and Multivariate Model-Based Clustering in Group-Specific Functional Subspaces | 2.3.1 | 2.3.1 |
fUnitRoots Rmetrics - Modelling Trends and Unit Roots | 3042.79 | 3042.79 |
funLBM Model-Based Co-Clustering of Functional Data | 2.3 | 2.3 |
funtimes Functions for Time Series Analysis | 9.1 | 9.1 |
furrr Apply Mapping Functions in Parallel using Futures | 0.3.1 | 0.3.1 |
futile.logger A Logging Utility for R | 1.4.3 | 1.4.3 |
futile.options Futile Options Management | 1.0.1 | 1.0.1 |
future Unified Parallel and Distributed Processing in R for Everyone | 1.33.1 | 1.33.1 |
future.apply Apply Function to Elements in Parallel using Futures | 1.11.1 | 1.11.1 |
future.batchtools A Future API for Parallel and Distributed Processing using 'batchtools' | 0.12.1 | 0.12.1 |
fuzzyjoin Join Tables Together on Inexact Matching | 0.1.6 | 0.1.6 |
FuzzyNumbers Tools to Deal with Fuzzy Numbers | 0.4-7 | 0.4-7 |
FuzzyNumbers.Ext.2 Apply Two Fuzzy Numbers on a Monotone Function | 3.2 | 3.2 |
FuzzyR Fuzzy Logic Toolkit for R | 2.3.2 | 2.3.2 |
fwildclusterboot Fast Wild Cluster Bootstrap Inference for Linear Models | 0.13.0 | 0.13.0 |
fxregime Exchange Rate Regime Analysis | 1.0-4 | 1.0-4 |
g.data Delayed-Data Packages | 2.4 | 2.4 |
GA Genetic Algorithms | 3.2.3 | 3.2.3 |
GAD Analysis of Variance from General Principles | 1.1.1 | 1.1.1 |
gafit Genetic Algorithm for Curve Fitting | 0.5.1 | 0.5.1 |
gam Generalized Additive Models | 1.22-3 | 1.22-3 |
gamair Data for 'GAMs: An Introduction with R' | 1.0-2 | 1.0-2 |
gambin Fit the Gambin Model to Species Abundance Distributions | 2.5.0 | 2.5.0 |
gamboostLSS Boosting Methods for 'GAMLSS' | 2.0-7 | 2.0-7 |
gamboostMSM Boosting Multistate Models | 1.1.88 | 1.1.88 |
gamlr Gamma Lasso Regression | 1.13-8 | 1.13-8 |
gamlss Generalized Additive Models for Location Scale and Shape | 5.4-20 | 5.4-20 |
gamlss.cens Fitting an Interval Response Variable Using `gamlss.family' Distributions | 5.0-7 | 5.0-7 |
gamlss.data Data for Generalised Additive Models for Location Scale and Shape | 6.0-2 | 6.0-2 |
gamlss.dist Distributions for Generalized Additive Models for Location Scale and Shape | 6.1-1 | 6.1-1 |
gamm4 Generalized Additive Mixed Models using 'mgcv' and 'lme4' | 0.2-6 | 0.2-6 |
ganalytics Interact with 'Google Analytics' | 0.10.7 | 0.10.7 |
gap Genetic Analysis Package | 1.5-3 | 1.5-3 |
gap.datasets Datasets for 'gap' | 0.0.6 | 0.0.6 |
gapfill Fill Missing Values in Satellite Data | 0.9.6-1 | 0.9.6-1 |
GARCHSK Estimating a GARCHSK Model and GJRSK Model | 0.1.0 | 0.1.0 |
garchx Flexible and Robust GARCH-X Modelling | 1.5 | 1.5 |
gargle Utilities for Working with Google APIs | 1.5.1 | 1.5.1 |
garma Fitting and Forecasting Gegenbauer ARMA Time Series Models | 0.9.13 | 0.9.13 |
GAS Generalized Autoregressive Score Models | 0.3.4 | 0.3.4 |
gaston Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models | 1.5.7 | 1.5.7 |
gaussDiff Difference measures for multivariate Gaussian probability density functions | 1.1 | 1.1 |
gaussquad Collection of Functions for Gaussian Quadrature | 1.0-3 | 1.0-3 |
gazepath Parse Eye-Tracking Data into Fixations | 1.3 | 1.3 |
gb Generalize Lambda Distribution and Generalized Bootstrapping | 2.3.3 | 2.3.3 |
GB2 Generalized Beta Distribution of the Second Kind: Properties, Likelihood, Estimation | 2.1.1 | 2.1.1 |
gbm Generalized Boosted Regression Models | 2.1.9 | 2.1.9 |
gbRd Utilities for Processing Rd Objects and Files | 0.4-11 | 0.4-11 |
gbutils Utilities for Simulation, Plots, Quantile Functions and Programming | 0.5 | 0.5 |
gcbd 'GPU'/CPU Benchmarking in Debian-Based Systems | 0.2.6 | 0.2.6 |
gcerisk Generalized Competing Event Model | 19.05.24 | 19.05.24 |
gclus Clustering Graphics | 1.3.2 | 1.3.2 |
GCPM Generalized Credit Portfolio Model | 1.2.2 | 1.2.2 |
gcrma | 2.70.0 | 2.70.0 |
gdalUtilities Wrappers for 'GDAL' Utilities Executables | 1.2.5 | 1.2.5 |
gdata Various R Programming Tools for Data Manipulation | 3.0.0 | 3.0.0 |
GDINA The Generalized DINA Model Framework | 2.9.4 | 2.9.4 |
gdistance Distances and Routes on Geographical Grids | 1.6.4 | 1.6.4 |
gdpc Generalized Dynamic Principal Components | 1.1.2 | 1.1.2 |
gdtools Utilities for Graphical Rendering and Fonts Management | 0.3.5 | 0.3.5 |
gear Geostatistical Analysis in R | 0.3.4 | 0.3.4 |
gee Generalized Estimation Equation Solver | 4.13-26 | 4.13-26 |
geeM Solve Generalized Estimating Equations | 0.10.1 | 0.10.1 |
geepack Generalized Estimating Equation Package | 1.3.12 | 1.3.12 |
geigen Calculate Generalized Eigenvalues, the Generalized Schur Decomposition and the Generalized Singular Value Decomposition of a Matrix Pair with Lapack | 2.3 | 2.3 |
geiger Analysis of Evolutionary Diversification | 2.0.11 | 2.0.11 |
gemtc Network Meta-Analysis Using Bayesian Methods | 1.0-2 | 1.0-2 |
GenABEL genome-wide SNP association analysis | 1.8-0 | 1.8-0 |
GenABEL.data Package contains data which is used by GenABEL example and test<U+000a>functions | 1.0.0 | 1.0.0 |
genalg R Based Genetic Algorithm | 0.2.1 | 0.2.1 |
GenBinomApps Clopper-Pearson Confidence Interval and Generalized Binomial Distribution | 1.2 | 1.2 |
gender Predict Gender from Names Using Historical Data | 0.6.0 | 0.6.0 |
gendist Generated Probability Distribution Models | 2.0 | 2.0 |
GENEAread Package for Reading Binary Files | 2.0.10 | 2.0.10 |
genefilter | 1.86.0 | 1.86.0 |
GeneNet Modeling and Inferring Gene Networks | 1.2.16 | 1.2.16 |
geneplotter | 1.76.0 | 1.76.0 |
generalCorr Generalized Correlations, Causal Paths and Portfolio Selection | 1.2.6 | 1.2.6 |
GeneralizedHyperbolic The Generalized Hyperbolic Distribution | 0.8-6 | 0.8-6 |
GeneralizedUmatrix Credible Visualization for Two-Dimensional Projections of Data | 1.2.6 | 1.2.6 |
generics Common S3 Generics not Provided by Base R Methods Related to Model Fitting | 0.1.3 | 0.1.3 |
genesysr Genesys PGR Client | 1.0.0 | 1.0.0 |
genetics Population Genetics | 1.3.8.1.3 | 1.3.8.1.3 |
genie Fast, Robust, and Outlier Resistant Hierarchical Clustering | 1.0.5 | 1.0.5 |
genieclust Fast and Robust Hierarchical Clustering with Noise Points Detection | 1.1.5-2 | 1.1.5-2 |
genlasso Path Algorithm for Generalized Lasso Problems | 1.5 | 1.5 |
GENMETA Implements Generalized Meta-Analysis Using Iterated Reweighted Least Squares Algorithm | 0.2.0 | 0.2.0 |
GenomeInfoDb | 1.40.1 | 1.40.1 |
GenomeInfoDbData | 1.2.12 | 1.2.12 |
GenomicAlignments | 1.34.0 | 1.34.0 |
GenomicFeatures | 1.50.3 | 1.50.3 |
GenomicRanges | 1.56.2 | 1.56.2 |
genoPlotR Plot Publication-Grade Gene and Genome Maps | 0.8.11 | 0.8.11 |
GenOrd Simulation of Discrete Random Variables with Given Correlation Matrix and Marginal Distributions | 1.4.0 | 1.4.0 |
GenSA R Functions for Generalized Simulated Annealing | 1.1.14 | 1.1.14 |
genSurv Generating Multi-State Survival Data | 1.0.4 | 1.0.4 |
geodist Fast, Dependency-Free Geodesic Distance Calculations | 0.0.8 | 0.0.8 |
geofd Spatial Prediction for Function Value Data | 2.0 | 2.0 |
geogrid Turn Geospatial Polygons into Regular or Hexagonal Grids | 0.1.2 | 0.1.2 |
geojson Classes for 'GeoJSON' | 0.3.5 | 0.3.5 |
geojsonio Convert Data from and to 'GeoJSON' or 'TopoJSON' | 0.11.3 | 0.11.3 |
geojsonlint Tools for Validating 'GeoJSON' | 0.4.0 | 0.4.0 |
geojsonsf GeoJSON to Simple Feature Converter | 2.0.3 | 2.0.3 |
geoknife Web-Processing of Large Gridded Datasets | 1.6.10 | 1.6.10 |
GEOmap Topographic and Geologic Mapping | 2.5-11 | 2.5-11 |
geomapdata Data for Topographic and Geologic Mapping | 2.0-2 | 2.0-2 |
geometa Tools for Reading and Writing ISO/OGC Geographic Metadata | 0.7-1 | 0.7-1 |
geometries Convert Between R Objects and Geometric Structures | 0.2.4 | 0.2.4 |
geometry Mesh Generation and Surface Tessellation | 0.4.7 | 0.4.7 |
geomorph Geometric Morphometric Analyses of 2D and 3D Landmark Data | 4.0.6 | 4.0.6 |
geonames Interface to the "Geonames" Spatial Query Web Service | 0.999 | 0.999 |
geonapi 'GeoNetwork' API R Interface | 0.7 | 0.7 |
GEOquery | 2.72.0 | 2.72.0 |
geos Open Source Geometry Engine ('GEOS') R API | 0.2.2 | 0.2.2 |
geosapi GeoServer REST API R Interface | 0.6-7 | 0.6-7 |
geosphere Spherical Trigonometry | 1.5-18 | 1.5-18 |
geospt Geostatistical Analysis and Design of Optimal Spatial Sampling Networks | 1.0-3 | 1.0-3 |
geotopbricks An R Plug-in for the Distributed Hydrological Model GEOtop | 1.5.8.0 | 1.5.8.0 |
geouy Geographic Information of Uruguay | 0.2.8 | 0.2.8 |
gert Simple Git Client for R | 2.0.1 | 2.0.1 |
getmstatistic Quantifying Systematic Heterogeneity in Meta-Analysis | 0.2.2 | 0.2.2 |
getopt C-Like 'getopt' Behavior | 1.20.4 | 1.20.4 |
GetoptLong Parsing Command-Line Arguments and Simple Variable Interpolation | 1.0.5 | 1.0.5 |
getPass Masked User Input | 0.2-4 | 0.2-4 |
gets General-to-Specific (GETS) Modelling and Indicator Saturation Methods | 0.37 | 0.37 |
getspres SPRE Statistics for Exploring Heterogeneity in Meta-Analysis | 0.2.0 | 0.2.0 |
GetTDData Get Data for Brazilian Bonds (Tesouro Direto) | 1.5.4 | 1.5.4 |
gfonts Offline 'Google' Fonts for 'Markdown' and 'Shiny' | 0.2.0 | 0.2.0 |
gfoRmula Parametric G-Formula | 1.0.3 | 1.0.3 |
ggalluvial Alluvial Plots in 'ggplot2' | 0.12.5 | 0.12.5 |
GGally Extension to 'ggplot2' | 2.2.0 | 2.2.0 |
ggalt Extra Coordinate Systems, 'Geoms', Statistical Transformations, Scales and Fonts for 'ggplot2' | 0.4.0 | 0.4.0 |
ggamma Generalized Gamma Probability Distribution | 1.0.1 | 1.0.1 |
gganimate A Grammar of Animated Graphics | 1.0.8 | 1.0.8 |
ggbeeswarm Categorical Scatter (Violin Point) Plots | 0.7.2 | 0.7.2 |
ggdag Analyze and Create Elegant Directed Acyclic Graphs | 0.2.13 | 0.2.13 |
ggdemetra 'ggplot2' Extension for Seasonal and Trading Day Adjustment with 'RJDemetra' | 0.2.7 | 0.2.7 |
ggdendro Create Dendrograms and Tree Diagrams Using 'ggplot2' | 0.1.23 | 0.1.23 |
ggdist Visualizations of Distributions and Uncertainty | 3.3.1 | 3.3.1 |
ggeffects Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs | 1.3.4 | 1.3.4 |
ggExtra Add Marginal Histograms to 'ggplot2', and More 'ggplot2' Enhancements | 0.10.1 | 0.10.1 |
ggfittext Fit Text Inside a Box in 'ggplot2' | 0.10.2 | 0.10.2 |
ggforce Accelerating 'ggplot2' | 0.4.1 | 0.4.1 |
ggformula Formula Interface to the Grammar of Graphics | 0.12.0 | 0.12.0 |
ggfortify Data Visualization Tools for Statistical Analysis Results | 0.4.16 | 0.4.16 |
ggfun Miscellaneous Functions for 'ggplot2' | 0.1.4 | 0.1.4 |
gghalves Compose Half-Half Plots Using Your Favourite Geoms | 0.1.4 | 0.1.4 |
ggimage Use Image in 'ggplot2' | 0.3.3 | 0.3.3 |
GGIR Raw Accelerometer Data Analysis | 3.0-3 | 3.0-3 |
ggiraph Make 'ggplot2' Graphics Interactive | 0.8.10 | 0.8.10 |
GGIRread Wearable Accelerometer Data File Readers | 1.0.2 | 1.0.2 |
ggmap Spatial Visualization with ggplot2 | 4.0.0 | 4.0.0 |
ggmcmc Tools for Analyzing MCMC Simulations from Bayesian Inference | 1.5.1.1 | 1.5.1.1 |
ggnetwork Geometries to Plot Networks with 'ggplot2' | 0.5.12 | 0.5.12 |
ggnewscale Multiple Fill and Colour Scales in 'ggplot2' | 0.4.9 | 0.4.9 |
ggpath Robust Image Rendering Support for 'ggplot2' | 1.0.1 | 1.0.1 |
ggplot.multistats Multiple Summary Statistics for Binned Stats/Geometries | 1.0.0 | 1.0.0 |
ggplot2 Create Elegant Data Visualisations Using the Grammar of Graphics | 3.4.4 | 3.4.4 |
ggplot2movies Movies Data | 0.0.1 | 0.0.1 |
ggplotify Convert Plot to 'grob' or 'ggplot' Object | 0.1.2 | 0.1.2 |
ggpmisc Miscellaneous Extensions to 'ggplot2' | 0.5.5 | 0.5.5 |
ggpp Grammar Extensions to 'ggplot2' | 0.5.6 | 0.5.6 |
ggpubr 'ggplot2' Based Publication Ready Plots | 0.6.0 | 0.6.0 |
ggquiver Quiver Plots for 'ggplot2' | 0.3.3 | 0.3.3 |
ggraph An Implementation of Grammar of Graphics for Graphs and Networks | 2.0.6 | 2.0.6 |
ggrastr Rasterize Layers for 'ggplot2' | 1.0.1 | 1.0.1 |
ggrepel Automatically Position Non-Overlapping Text Labels with 'ggplot2' | 0.9.5 | 0.9.5 |
ggridges Ridgeline Plots in 'ggplot2' | 0.5.6 | 0.5.6 |
ggsci Scientific Journal and Sci-Fi Themed Color Palettes for 'ggplot2' | 3.0.0 | 3.0.0 |
ggseas 'stats' for Seasonal Adjustment on the Fly with 'ggplot2' | 0.5.4 | 0.5.4 |
ggseqlogo A 'ggplot2' Extension for Drawing Publication-Ready Sequence Logos | 0.1 | 0.1 |
ggsignif Significance Brackets for 'ggplot2' | 0.6.4 | 0.6.4 |
ggsn North Symbols and Scale Bars for Maps Created with 'ggplot2' or 'ggmap' | 0.5.0 | 0.5.0 |
ggsoccer Plot Soccer Event Data | 0.1.7 | 0.1.7 |
ggspatial Spatial Data Framework for ggplot2 | 1.1.9 | 1.1.9 |
ggspectra Extensions to 'ggplot2' for Radiation Spectra | 0.3.13 | 0.3.13 |
ggstance Horizontal 'ggplot2' Components | 0.3.7 | 0.3.7 |
ggstats Extension to 'ggplot2' for Plotting Stats | 0.5.1 | 0.5.1 |
ggtext Improved Text Rendering Support for 'ggplot2' | 0.1.2 | 0.1.2 |
ggthemes Extra Themes, Scales and Geoms for 'ggplot2' | 5.0.0 | 5.0.0 |
ggtree | 3.12.0 | 3.12.0 |
ggvis Interactive Grammar of Graphics | 0.4.8 | 0.4.8 |
ggvoronoi Voronoi Diagrams and Heatmaps with 'ggplot2' | 0.8.5 | 0.8.5 |
gh 'GitHub' 'API' | 1.4.0 | 1.4.0 |
ghyp Generalized Hyperbolic Distribution and Its Special Cases | 1.6.4 | 1.6.4 |
Gifi Multivariate Analysis with Optimal Scaling | 0.4-0 | 0.4-0 |
gifski Highest Quality GIF Encoder | 1.32.0-1 | 1.32.0-1 |
gifti Reads in 'Neuroimaging' 'GIFTI' Files with Geometry Information | 0.8.0 | 0.8.0 |
GIGrvg Random Variate Generator for the GIG Distribution | 0.8 | 0.8 |
GillespieSSA Gillespie's Stochastic Simulation Algorithm (SSA) | 0.6.2 | 0.6.2 |
gimme Group Iterative Multiple Model Estimation | 0.7-15 | 0.7-15 |
giscoR Download Map Data from GISCO API - Eurostat | 0.4.0 | 0.4.0 |
gistr Work with 'GitHub' 'Gists' | 0.9.0 | 0.9.0 |
git2r Provides Access to Git Repositories | 0.33.0 | 0.33.0 |
gitcreds Query 'git' Credentials from 'R' | 0.1.2 | 0.1.2 |
gitlabr Access to the 'GitLab' API | 2.0.1 | 2.0.1 |
GJRM Generalised Joint Regression Modelling | 0.2-6.5 | 0.2-6.5 |
gk g-and-k and g-and-h Distribution Functions | 0.6.0 | 0.6.0 |
glarma Generalized Linear Autoregressive Moving Average Models | 1.6-0 | 1.6-0 |
GlarmaVarSel Variable Selection in Sparse GLARMA Models | 1.0 | 1.0 |
glasso Graphical Lasso: Estimation of Gaussian Graphical Models | 1.11 | 1.11 |
glassoFast Fast Graphical LASSO | 1.0.1 | 1.0.1 |
gld Estimation and Use of the Generalised (Tukey) Lambda Distribution | 2.6.6 | 2.6.6 |
GLDEX Fitting Single and Mixture of Generalised Lambda Distributions | 2.0.0.9.3 | 2.0.0.9.3 |
glinternet Learning Interactions via Hierarchical Group-Lasso Regularization | 1.0.12 | 1.0.12 |
glm2 Fitting Generalized Linear Models | 1.2.1 | 1.2.1 |
GLMMadaptive Generalized Linear Mixed Models using Adaptive Gaussian Quadrature | 0.8-5 | 0.8-5 |
glmmfields Generalized Linear Mixed Models with Robust Random Fields for Spatiotemporal Modeling | 0.1.4 | 0.1.4 |
glmmML Generalized Linear Models with Clustering | 1.1.6 | 1.1.6 |
GLMMRR Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data | 0.5.0 | 0.5.0 |
glmmTMB Generalized Linear Mixed Models using Template Model Builder | 1.1.10 | 1.1.10 |
glmnet Lasso and Elastic-Net Regularized Generalized Linear Models | 4.1-8 | 4.1-8 |
glmpath L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model | 0.98 | 0.98 |
glmx Generalized Linear Models Extended | 0.2-0 | 0.2-0 |
GlobalOptions Generate Functions to Get or Set Global Options | 0.1.2 | 0.1.2 |
globalOptTests Objective functions for benchmarking the performance of global optimization algorithms | 1.1 | 1.1 |
globals Identify Global Objects in R Expressions | 0.16.2 | 0.16.2 |
glogis Fitting and Testing Generalized Logistic Distributions | 1.0-2 | 1.0-2 |
glpkAPI R Interface to C API of GLPK | 1.3.4 | 1.3.4 |
glue Interpreted String Literals | 1.7.0 | 1.7.0 |
gma Granger Mediation Analysis | 1.0 | 1.0 |
gmailr Access the 'Gmail' 'RESTful' API | 2.0.0 | 2.0.0 |
gmaps Wrapper and auxilliary functions for maps package to work with grid graphics system. | 0.2 | 0.2 |
GMCM Fast Estimation of Gaussian Mixture Copula Models | 1.4 | 1.4 |
GMDH Short Term Forecasting via GMDH-Type Neural Network Algorithms | 1.6 | 1.6 |
Gmedian Geometric Median, k-Medians Clustering and Robust Median PCA | 1.2.7 | 1.2.7 |
gmeta Meta-Analysis via a Unified Framework of Confidence Distribution | 2.3-1 | 2.3-1 |
gmm Generalized Method of Moments and Generalized Empirical Likelihood | 1.8 | 1.8 |
GMMBoost Likelihood-Based Boosting for Generalized Mixed Models | 1.1.5 | 1.1.5 |
gmnl Multinomial Logit Models with Random Parameters | 1.1-3.2 | 1.1-3.2 |
gmodels Various R Programming Tools for Model Fitting | 2.18.1.1 | 2.18.1.1 |
gmp Multiple Precision Arithmetic | 0.7-2 | 0.7-2 |
gmpoly Multivariate Polynomials with Rational Coefficients | 1.1.0 | 1.1.0 |
gmt Interface Between GMT Map-Making Software and R | 2.0.3 | 2.0.3 |
gmvarkit Estimate Gaussian and Student's t Mixture Vector Autoregressive Models | 2.1.1 | 2.1.1 |
GNAR Methods for Fitting Network Time Series Models | 1.1.3 | 1.1.3 |
gnm Generalized Nonlinear Models | 1.1-5 | 1.1-5 |
gnorm Generalized Normal/Exponential Power Distribution | 1.0.0 | 1.0.0 |
GO.db | 3.19.1 | 3.19.1 |
goftest Classical Goodness-of-Fit Tests for Univariate Distributions | 1.2-3 | 1.2-3 |
gogarch Generalized Orthogonal GARCH (GO-GARCH) Models | 0.7-5 | 0.7-5 |
googleAnalyticsR Google Analytics API into R | 1.0.1 | 1.0.1 |
googleAuthR Authenticate and Create Google APIs | 2.0.1 | 2.0.1 |
googleCloudStorageR Interface with Google Cloud Storage API | 0.7.0 | 0.7.0 |
googleComputeEngineR R Interface with Google Compute Engine | 0.3.0 | 0.3.0 |
googledrive An Interface to Google Drive | 2.1.1 | 2.1.1 |
googleLanguageR Call Google's 'Natural Language' API, 'Cloud Translation' API, 'Cloud Speech' API and 'Cloud Text-to-Speech' API | 0.3.0 | 0.3.0 |
googlePolylines Encoding Coordinates into 'Google' Polylines | 0.8.4 | 0.8.4 |
googlesheets4 Access Google Sheets using the Sheets API V4 | 1.1.1 | 1.1.1 |
googleVis R Interface to Google Charts | 0.7.1 | 0.7.1 |
googleway Accesses Google Maps APIs to Retrieve Data and Plot Maps | 2.7.8 | 2.7.8 |
GOplot Visualization of Functional Analysis Data | 1.0.2 | 1.0.2 |
GORCure Fit Generalized Odds Rate Mixture Cure Model with Interval Censored Data | 2.0 | 2.0 |
GOSemSim | 2.30.2 | 2.30.2 |
gower Gower's Distance | 1.0.0 | 1.0.0 |
GPareto Gaussian Processes for Pareto Front Estimation and Optimization | 1.1.8 | 1.1.8 |
GPArotation Gradient Projection Factor Rotation | 2023.11-1 | 2023.11-1 |
GPCMlasso Differential Item Functioning in Generalized Partial Credit Models | 0.1-6 | 0.1-6 |
GPFDA Gaussian Process for Functional Data Analysis | 3.1.3 | 3.1.3 |
GPfit Gaussian Processes Modeling | 1.0-8 | 1.0-8 |
gplots Various R Programming Tools for Plotting Data | 3.1.3 | 3.1.3 |
gprofiler2 Interface to the 'g:Profiler' Toolset | 0.2.2 | 0.2.2 |
GramQuad Gram Quadrature | 0.1.1 | 0.1.1 |
granova Graphical Analysis of Variance | 2.1 | 2.1 |
graphicalExtremes Statistical Methodology for Graphical Extreme Value Models | 0.3.0 | 0.3.0 |
graphicalVAR Graphical VAR for Experience Sampling Data | 0.3.3 | 0.3.3 |
graphics | 4.4.1 | 4.4.1 |
graphlayouts Additional Layout Algorithms for Network Visualizations | 1.1.0 | 1.1.0 |
graphTweets Visualise Twitter Interactions | 0.5.3 | 0.5.3 |
grates Grouped Date Classes | 1.1.0 | 1.1.0 |
gratis Generating Time Series with Diverse and Controllable Characteristics | 1.0.5 | 1.0.5 |
gravitas Explore Probability Distributions for Bivariate Temporal Granularities | 0.1.3 | 0.1.3 |
gravity Estimation Methods for Gravity Models | 1.1 | 1.1 |
grDevices | 4.4.1 | 4.4.1 |
greeks Sensitivities of Prices of Financial Options and Implied Volatilities | 1.3.2 | 1.3.2 |
gregmisc Greg's Miscellaneous Functions | 2.1.5 | 2.1.5 |
GREMLINS Generalized Multipartite Networks | 0.2.1 | 0.2.1 |
greta Simple and Scalable Statistical Modelling in R | 0.4.5 | 0.4.5 |
greybox Toolbox for Model Building and Forecasting | 2.0.0 | 2.0.0 |
grf Generalized Random Forests | 2.3.1 | 2.3.1 |
grid The Grid Graphics Package | 4.4.1 | 4.4.1 |
gridBase Integration of base and grid graphics | 0.4-7 | 0.4-7 |
gridExtra Miscellaneous Functions for "Grid" Graphics | 2.3 | 2.3 |
gridGraphics Redraw Base Graphics Using 'grid' Graphics | 0.5-1 | 0.5-1 |
gridSVG Export 'grid' Graphics as SVG | 1.7-5 | 1.7-5 |
gridtext Improved Text Rendering Support for 'Grid' Graphics | 0.1.5 | 0.1.5 |
grImport2 Importing 'SVG' Graphics | 0.3-1 | 0.3-1 |
grnn General regression neural network | 0.1.0 | 0.1.0 |
groundhog Version-Control for CRAN, GitHub, and GitLab Packages | 3.1.2 | 3.1.2 |
GroupSeq Group Sequential Design Probabilities - With Graphical User Interface | 1.4.0 | 1.4.0 |
growfunctions Bayesian Non-Parametric Dependent Models for Time-Indexed Functional Data | 0.16 | 0.16 |
grplasso Fitting User-Specified Models with Group Lasso Penalty | 0.4-7 | 0.4-7 |
grpreg Regularization Paths for Regression Models with Grouped Covariates | 3.4.0 | 3.4.0 |
grwat River Hydrograph Separation and Analysis | 0.0.4 | 0.0.4 |
gsarima Two Functions for Generalized SARIMA Time Series Simulation | 0.1-5 | 0.1-5 |
GSE Robust Estimation in the Presence of Cellwise and Casewise Contamination and Missing Data | 4.2-1 | 4.2-1 |
gSEM Semi-Supervised Generalized Structural Equation Modeling | 0.4.3.4 | 0.4.3.4 |
gset Group Sequential Design in Equivalence Studies | 1.1.0 | 1.1.0 |
gsheet Download Google Sheets Using Just the URL | 0.4.5 | 0.4.5 |
gsignal Signal Processing | 0.3-5 | 0.3-5 |
gsisdecoder High Efficient Functions to Decode NFL Player IDs | 0.0.1 | 0.0.1 |
gsl Wrapper for the Gnu Scientific Library | 2.1-8 | 2.1-8 |
gslnls GSL Multi-Start Nonlinear Least-Squares Fitting | 1.2.0 | 1.2.0 |
GSM Gamma Shape Mixture | 1.3.2 | 1.3.2 |
GSODR Global Surface Summary of the Day ('GSOD') Weather Data Client | 3.1.9 | 3.1.9 |
gson Base Class and Methods for 'gson' Format | 0.1.0 | 0.1.0 |
gss General Smoothing Splines | 2.2-7 | 2.2-7 |
GSSE Genotype-Specific Survival Estimation | 0.1 | 0.1 |
gstat Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation | 2.1-1 | 2.1-1 |
gsubfn Utilities for Strings and Function Arguments | 0.7 | 0.7 |
gsw Gibbs Sea Water Functions | 1.0-6 | 1.0-6 |
gsynth Generalized Synthetic Control Method | 1.2.1 | 1.2.1 |
gt Easily Create Presentation-Ready Display Tables | 0.11.1 | 0.11.1 |
gtable Arrange 'Grobs' in Tables | 0.3.4 | 0.3.4 |
gte Generalized Turnbull's Estimator | 1.2-3 | 1.2-3 |
gtfs2gps Converting Transport Data from GTFS Format to GPS-Like Records | 2.1-1 | 2.1-1 |
gtfsio Read and Write General Transit Feed Specification (GTFS) Files | 1.1.1 | 1.1.1 |
gtfstools General Transit Feed Specification (GTFS) Editing and Analysing Tools | 1.2.0 | 1.2.0 |
gtheory Apply Generalizability Theory with R | 0.1.2 | 0.1.2 |
gtools Various R Programming Tools | 3.9.5 | 3.9.5 |
gtop Game-Theoretically OPtimal (GTOP) Reconciliation Method | 0.2.0 | 0.2.0 |
gtrendsR Perform and Display Google Trends Queries | 1.5.1 | 1.5.1 |
gtsummary Presentation-Ready Data Summary and Analytic Result Tables | 2.0.3 | 2.0.3 |
GUIDE GUI for DErivatives in R | 1.2.7 | 1.2.7 |
gumbel The Gumbel-Hougaard Copula | 1.10-2 | 1.10-2 |
GUniFrac Generalized UniFrac Distances, Distance-Based Multivariate Methods and Feature-Based Univariate Methods for Microbiome Data Analysis | 1.8 | 1.8 |
gustave A User-Oriented Statistical Toolkit for Analytical Variance Estimation | 1.0.0 | 1.0.0 |
gvc Global Value Chains Tools | 6.4.0 | 6.4.0 |
GWI Count and Continuous Generalized Variability Indexes | 1.0.2 | 1.0.2 |
gWidgets gWidgets API for Building Toolkit-Independent, Interactive GUIs | 0.0-54.2 | 0.0-54.2 |
gWidgets2 Rewrite of gWidgets API for Simplified GUI Construction | 1.0-9 | 1.0-9 |
gWidgets2tcltk Toolkit Implementation of gWidgets2 for tcltk | 1.0-8 | 1.0-8 |
gWidgetstcltk Toolkit implementation of gWidgets for tcltk package | 0.0-55.1 | 0.0-55.1 |
GWmodel Geographically-Weighted Models | 2.3-1 | 2.3-1 |
gwrr Fits Geographically Weighted Regression Models with Diagnostic Tools | 0.2-2 | 0.2-2 |
GWSDAT GroundWater Spatiotemporal Data Analysis Tool (GWSDAT) | 3.2.0 | 3.2.0 |
h2o R Interface for the 'H2O' Scalable Machine Learning Platform | 3.36.1.2 | 3.36.1.2 |
HAC Estimation, Simulation and Visualization of Hierarchical Archimedean Copulae (HAC) | 1.1-0 | 1.1-0 |
hackeRnews Wrapper for the 'Official Hacker News' API | 0.1.0 | 0.1.0 |
HandTill2001 Multiple Class Area under ROC Curve | 1.0.1 | 1.0.1 |
hapassoc Inference of Trait Associations with SNP Haplotypes and Other Attributes using the EM Algorithm | 1.2-9 | 1.2-9 |
haplo.stats Statistical Analysis of Haplotypes with Traits and Covariates when Linkage Phase is Ambiguous | 1.9.5 | 1.9.5 |
hardhat Construct Modeling Packages | 1.3.0 | 1.3.0 |
HardyWeinberg Statistical Tests and Graphics for Hardy-Weinberg Equilibrium | 1.7.5 | 1.7.5 |
harmonicmeanp Harmonic Mean p-Values and Model Averaging by Mean Maximum Likelihood | 3.0.1 | 3.0.1 |
harmony Fast, Sensitive, and Accurate Integration of Single Cell Data | 1.2.1 | 1.2.1 |
hash Full Featured Implementation of Hash Tables/Associative Arrays/Dictionaries | 3.0.1 | 3.0.1 |
haven Import and Export 'SPSS', 'Stata' and 'SAS' Files | 2.5.4 | 2.5.4 |
hbsae Hierarchical Bayesian Small Area Estimation | 1.2 | 1.2 |
HBV.IANIGLA Modular Hydrological Model | 0.2.6 | 0.2.6 |
hda Heteroscedastic Discriminant Analysis | 0.2-14 | 0.2-14 |
hdbm High Dimensional Bayesian Mediation Analysis | 0.9.0 | 0.9.0 |
HDclassif High Dimensional Supervised Classification and Clustering | 2.2.1 | 2.2.1 |
hdf5r Interface to the 'HDF5' Binary Data Format | 1.3.11 | 1.3.11 |
hdi High-Dimensional Inference | 0.1-9 | 0.1-9 |
HDInterval Highest (Posterior) Density Intervals | 0.2.4 | 0.2.4 |
hdm High-Dimensional Metrics | 0.3.1 | 0.3.1 |
hdnom Benchmarking and Visualization Toolkit for Penalized Cox Models | 6.0.2 | 6.0.2 |
HDO.db | 0.99.1 | 0.99.1 |
hdrcde Highest Density Regions and Conditional Density Estimation | 3.4 | 3.4 |
HDShOP High-Dimensional Shrinkage Optimal Portfolios | 0.1.3 | 0.1.3 |
HDTSA High Dimensional Time Series Analysis Tools | 1.0.2 | 1.0.2 |
HDtweedie The Lasso for Tweedie's Compound Poisson Model Using an IRLS-BMD Algorithm | 1.2 | 1.2 |
heatmaply Interactive Cluster Heat Maps Using 'plotly' and 'ggplot2' | 1.5.0 | 1.5.0 |
Heatplus | 3.12.0 | 3.12.0 |
hellno Providing 'stringsAsFactors=FALSE' Variants of 'data.frame()' and 'as.data.frame()' | 0.0.1 | 0.0.1 |
heplots Visualizing Hypothesis Tests in Multivariate Linear Models | 1.6.0 | 1.6.0 |
here A Simpler Way to Find Your Files | 1.0.1 | 1.0.1 |
hermite Generalized Hermite Distribution | 1.1.2 | 1.1.2 |
hett Heteroscedastic t-Regression | 0.3-3 | 0.3-3 |
hexbin Hexagonal Binning Routines | 1.28.3 | 1.28.3 |
hexView Viewing Binary Files | 0.3-4 | 0.3-4 |
hglm.data Data for the 'hglm' Package | 1.0-1 | 1.0-1 |
HGNChelper Identify and Correct Invalid HGNC Human Gene Symbols and MGI Mouse Gene Symbols | 0.8.1 | 0.8.1 |
hgu133plus2.db | 3.13.0 | 3.13.0 |
hgu133plus2cdf | 2.18.0 | 2.18.0 |
hgu95av2.db | 3.13.0 | 3.13.0 |
hgu95av2cdf | 2.18.0 | 2.18.0 |
HH Statistical Analysis and Data Display: Heiberger and Holland | 3.1-49 | 3.1-49 |
hht The Hilbert-Huang Transform: Tools and Methods | 2.1.6 | 2.1.6 |
HI Simulation from Distributions Supported by Nested Hyperplanes | 0.5 | 0.5 |
hierfstat Estimation and Tests of Hierarchical F-Statistics | 0.5-11 | 0.5-11 |
highcharter A Wrapper for the 'Highcharts' Library | 0.9.4 | 0.9.4 |
highfrequency Tools for Highfrequency Data Analysis | 1.0.1 | 1.0.1 |
highlight Syntax Highlighter | 0.5.1 | 0.5.1 |
highr Syntax Highlighting for R Source Code | 0.11 | 0.11 |
highs 'HiGHS' Optimization Solver | 0.1-10 | 0.1-10 |
hipread Read Hierarchical Fixed Width Files | 0.2.4 | 0.2.4 |
HistData Data Sets from the History of Statistics and Data Visualization | 0.8-7 | 0.8-7 |
histogram Construction of Regular and Irregular Histograms with Different Options for Automatic Choice of Bins | 0.0-25 | 0.0-25 |
HistogramTools Utility Functions for R Histograms | 0.3.2 | 0.3.2 |
hitandrun "Hit and Run" and "Shake and Bake" for Sampling Uniformly from Convex Shapes | 0.5-6 | 0.5-6 |
HLMdiag Diagnostic Tools for Hierarchical (Multilevel) Linear Models | 0.5.0 | 0.5.0 |
Hmisc Harrell Miscellaneous | 5.1-1 | 5.1-1 |
hms Pretty Time of Day | 1.1.3 | 1.1.3 |
hNMF Hierarchical Non-Negative Matrix Factorization | 1.0 | 1.0 |
hoardr Manage Cached Files | 0.5.4 | 0.5.4 |
homals Gifi Methods for Optimal Scaling | 1.0-10 | 1.0-10 |
hommel Methods for Closed Testing with Simes Inequality, in Particular Hommel's Method | 1.6 | 1.6 |
homologene Quick Access to Homologene and Gene Annotation Updates | 1.4.68.19.3.27 | 1.4.68.19.3.27 |
hoopR Access Men's Basketball Play by Play Data | 2.1.0 | 2.1.0 |
hot.deck Multiple Hot Deck Imputation | 1.2 | 1.2 |
howzatR Useful Functions for Cricket Analysis | 1.0.1 | 1.0.1 |
HPO.db A set of annotation maps describing the Human Phenotype Ontology | 0.99.2 | 0.99.2 |
hqreg Regularization Paths for Lasso or Elastic-Net Penalized Huber Loss Regression and Quantile Regression | 1.4 | 1.4 |
hrbrthemes Additional Themes, Theme Components and Utilities for 'ggplot2' | 0.8.0 | 0.8.0 |
hrqglas Group Variable Selection for Quantile and Robust Mean Regression | 1.1.0 | 1.1.0 |
HSAUR3 A Handbook of Statistical Analyses Using R (3rd Edition) | 1.0-14 | 1.0-14 |
htm2txt Convert Html into Text | 2.2.2 | 2.2.2 |
htmlTable Advanced Tables for Markdown/HTML | 2.4.2 | 2.4.2 |
htmltidy Tidy Up and Test XPath Queries on HTML and XML Content | 0.5.0 | 0.5.0 |
htmltools Tools for HTML | 0.5.7 | 0.5.7 |
HTMLUtils Facilitates Automated HTML Report Creation | 0.1.9 | 0.1.9 |
htmlwidgets HTML Widgets for R | 1.6.4 | 1.6.4 |
hts Hierarchical and Grouped Time Series | 6.0.2 | 6.0.2 |
httpcache Query Cache for HTTP Clients | 1.2.0 | 1.2.0 |
httpcode 'HTTP' Status Code Helper | 0.3.0 | 0.3.0 |
httping 'Ping' 'URLs' to Time 'Requests' | 0.2.0 | 0.2.0 |
httpRequest Basic HTTP Request | 0.0.11 | 0.0.11 |
httptest A Test Environment for HTTP Requests | 4.2.2 | 4.2.2 |
httpuv HTTP and WebSocket Server Library | 1.6.14 | 1.6.14 |
httr Tools for Working with URLs and HTTP | 1.4.7 | 1.4.7 |
httr2 Perform HTTP Requests and Process the Responses | 1.0.0 | 1.0.0 |
huge High-Dimensional Undirected Graph Estimation | 1.3.5 | 1.3.5 |
humanFormat Human-Friendly Formatting Functions | 1.2 | 1.2 |
humanize Create Values for Human Consumption | 0.2.0 | 0.2.0 |
humidity Calculate Water Vapor Measures from Temperature and Dew Point | 0.1.5 | 0.1.5 |
hunspell High-Performance Stemmer, Tokenizer, and Spell Checker | 3.0.3 | 3.0.3 |
hutils Miscellaneous R Functions and Aliases | 1.8.1 | 1.8.1 |
huxtable Easily Create and Style Tables for LaTeX, HTML and Other Formats | 5.5.3 | 5.5.3 |
hwriter HTML Writer - Outputs R Objects in HTML Format | 1.3.2.1 | 1.3.2.1 |
hwwntest Tests of White Noise using Wavelets | 1.3.2 | 1.3.2 |
hydroApps Tools and models for hydrological applications | 0.1-1 | 0.1-1 |
hydrogeo Groundwater Data Presentation and Interpretation | 0.6-1 | 0.6-1 |
hydroGOF Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series | 0.5-4 | 0.5-4 |
hydroloom Utilities to Weave Hydrologic Fabrics | 1.0.2 | 1.0.2 |
HydroMe Estimating Water Retention and Infiltration Model Parameters using Experimental Data | 2.0-1 | 2.0-1 |
hydropeak Detect and Characterize Sub-Daily Flow Fluctuations | 0.1.2 | 0.1.2 |
hydroPSO Particle Swarm Optimisation, with Focus on Environmental Models | 0.5-1 | 0.5-1 |
hydroroute Trace Longitudinal Hydropeaking Waves | 0.1.2 | 0.1.2 |
hydroscoper Interface to the Greek National Data Bank for Hydrometeorological Information | 1.4.1 | 1.4.1 |
hydrostats Hydrologic Indices for Daily Time Series Data | 0.2.9 | 0.2.9 |
hydrotoolbox Hydrological Tools for Handling Hydro-Meteorological Data Records | 1.1.2 | 1.1.2 |
hydroTSM Time Series Management and Analysis for Hydrological Modelling | 0.7-0 | 0.7-0 |
hyfo Hydrology and Climate Forecasting | 1.4.3 | 1.4.3 |
hyper2 The Hyperdirichlet Distribution, Mark 2 | 3.0-0 | 3.0-0 |
HyperbolicDist The Hyperbolic Distribution | 0.6-5 | 0.6-5 |
hypergeo The Gauss Hypergeometric Function | 1.2-13 | 1.2-13 |
HypergeoMat Hypergeometric Function of a Matrix Argument | 4.0.2 | 4.0.2 |
hypervolume High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls | 3.0.4 | 3.0.4 |
i2extras Functions to Work with 'incidence2' Objects | 0.2.1 | 0.2.1 |
iai Interface to 'Interpretable AI' Modules | 1.10.0 | 1.10.0 |
iarm Item Analysis in Rasch Models | 0.4.3 | 0.4.3 |
ibd Incomplete Block Designs | 1.5 | 1.5 |
ibelief Belief Function Implementation | 1.3.1 | 1.3.1 |
ibmdbR IBM in-Database Analytics for R | 1.51.0 | 1.51.0 |
iBreakDown Model Agnostic Instance Level Variable Attributions | 2.1.2 | 2.1.2 |
IBrokers R API to Interactive Brokers Trader Workstation | 0.10-2 | 0.10-2 |
ic.infer Inequality Constrained Inference in Linear Normal Situations | 1.1-7 | 1.1-7 |
ica Independent Component Analysis | 1.0-3 | 1.0-3 |
ICAOD Optimal Designs for Nonlinear Statistical Models by Imperialist Competitive Algorithm (ICA) | 1.0.1 | 1.0.1 |
icarus Calibrates and Reweights Units in Samples | 0.3.2 | 0.3.2 |
ICBayes Bayesian Semiparametric Models for Interval-Censored Data | 1.2 | 1.2 |
ICC Facilitating Estimation of the Intraclass Correlation Coefficient | 2.4.0 | 2.4.0 |
iccbeta Multilevel Model Intraclass Correlation for Slope Heterogeneity | 1.2.0 | 1.2.0 |
ICEbox Individual Conditional Expectation Plot Toolbox | 1.1.5 | 1.1.5 |
iCellR Analyzing High-Throughput Single Cell Sequencing Data | 1.6.5 | 1.6.5 |
icenReg Regression Models for Interval Censored Data | 2.0.16 | 2.0.16 |
icensmis Study Design and Data Analysis in the Presence of Error-Prone Diagnostic Tests and Self-Reported Outcomes | 1.5.0 | 1.5.0 |
ICGE Estimation of Number of Clusters and Identification of Atypical Units | 0.4.2 | 0.4.2 |
ICGOR Fit Generalized Odds Rate Hazards Model with Interval Censored Data | 2.0 | 2.0 |
ichimoku Visualization and Tools for Ichimoku Kinko Hyo Strategies | 1.4.13 | 1.4.13 |
icRSF A Modified Random Survival Forest Algorithm | 1.2 | 1.2 |
ICS Tools for Exploring Multivariate Data via ICS/ICA | 1.4-1 | 1.4-1 |
ICSNP Tools for Multivariate Nonparametrics | 1.1-2 | 1.1-2 |
ICsurv Semiparametric Regression Analysis of Interval-Censored Data | 1.0.1 | 1.0.1 |
icsw Inverse Compliance Score Weighting | 1.0.0 | 1.0.0 |
ICtest Estimating and Testing the Number of Interesting Components in Linear Dimension Reduction | 0.3-5 | 0.3-5 |
idbr R Interface to the US Census Bureau International Data Base API | 1.0 | 1.0 |
IDE Integro-Difference Equation Spatio-Temporal Models | 0.3.1 | 0.3.1 |
idefix Efficient Designs for Discrete Choice Experiments | 1.0.3 | 1.0.3 |
idem Inference in Randomized Controlled Trials with Death and Missingness | 5.2 | 5.2 |
idendr0 Interactive Dendrograms | 1.5.3 | 1.5.3 |
IDPmisc 'Utilities of Institute of Data Analyses and Process Design (www.zhaw.ch/idp)' | 1.1.20 | 1.1.20 |
IDPSurvival Imprecise Dirichlet Process for Survival Analysis | 1.2 | 1.2 |
ids Generate Random Identifiers | 1.0.1 | 1.0.1 |
ie2misc Irucka Embry's Miscellaneous USGS Functions | 0.9.1 | 0.9.1 |
ifaTools Toolkit for Item Factor Analysis with 'OpenMx' | 0.23 | 0.23 |
igcop Computational Tools for the IG and IGL Copula Families | 1.0.2 | 1.0.2 |
igraph Network Analysis and Visualization | 2.0.1.1 | 2.0.1.1 |
imbibe A Pipe-Friendly Image Calculator | 0.1.1 | 0.1.1 |
imguR An Imgur.com API Client Package | 1.0.3 | 1.0.3 |
IMIFA Infinite Mixtures of Infinite Factor Analysers and Related Models | 2.2.0 | 2.2.0 |
immer Item Response Models for Multiple Ratings | 1.4-15 | 1.4-15 |
imp4p Imputation for Proteomics | 1.2 | 1.2 |
impimp Imprecise Imputation for Statistical Matching | 0.3.1 | 0.3.1 |
implied Convert Between Bookmaker Odds and Probabilities | 0.5 | 0.5 |
implyr R Interface for Apache Impala | 0.4.0 | 0.4.0 |
import An Import Mechanism for R | 1.3.2 | 1.3.2 |
imputeFin Imputation of Financial Time Series with Missing Values and/or Outliers | 0.1.2 | 0.1.2 |
imputeMulti Imputation Methods for Multivariate Multinomial Data | 0.8.4 | 0.8.4 |
imputeR A General Multivariate Imputation Framework | 2.2 | 2.2 |
imputeTestbench Test Bench for the Comparison of Imputation Methods | 3.0.3 | 3.0.3 |
imputeTS Time Series Missing Value Imputation | 3.3 | 3.3 |
imputeYn Imputing the Last Largest Censored Observation(s) Under Weighted Least Squares | 1.3 | 1.3 |
in2extRemes Into the extRemes Package | 1.0-3 | 1.0-3 |
inca Integer Calibration | 0.0.4 | 0.0.4 |
IncDTW Incremental Calculation of Dynamic Time Warping | 1.1.4.4 | 1.1.4.4 |
incidence Compute, Handle, Plot and Model Incidence of Dated Events | 1.7.3 | 1.7.3 |
incidence2 Compute, Handle and Plot Incidence of Dated Events | 2.2.3 | 2.2.3 |
inegiR Integrate INEGI’s (Mexican Stats Office) API with R | 3.0.0 | 3.0.0 |
ineq Measuring Inequality, Concentration, and Poverty | 0.2-13 | 0.2-13 |
infer Tidy Statistical Inference | 1.0.6 | 1.0.6 |
inferference Methods for Causal Inference with Interference | 1.0.2 | 1.0.2 |
inflection Finds the Inflection Point of a Curve | 1.3.6 | 1.3.6 |
influence.SEM Case Influence in Structural Equation Models | 2.3 | 2.3 |
influxdbr R Interface to InfluxDB | 0.14.2 | 0.14.2 |
InformativeCensoring Multiple Imputation for Informative Censoring | 0.3.6 | 0.3.6 |
infotheo Information-Theoretic Measures | 1.2.0.1 | 1.2.0.1 |
InfoTrad Calculates the Probability of Informed Trading (PIN) | 1.2 | 1.2 |
ingredients Effects and Importances of Model Ingredients | 2.3.0 | 2.3.0 |
ini Read and Write '.ini' Files | 0.3.1 | 0.3.1 |
inline Functions to Inline C, C++, Fortran Function Calls from R | 0.3.19 | 0.3.19 |
inlmisc Miscellaneous Functions for the USGS INL Project Office | 0.5.5 | 0.5.5 |
insee Tools to Easily Download Data from INSEE BDM Database | 1.1.5 | 1.1.5 |
insight Easy Access to Model Information for Various Model Objects | 0.19.8 | 0.19.8 |
InspectChangepoint High-Dimensional Changepoint Estimation via Sparse Projection | 1.2 | 1.2 |
instaR Access to Instagram API via R | 0.2.4 | 0.2.4 |
intamap Procedures for Automated Interpolation | 1.5-7 | 1.5-7 |
intccr Semiparametric Competing Risks Regression under Interval Censoring | 3.0.4 | 3.0.4 |
interactiveDisplayBase | 1.36.0 | 1.36.0 |
interflex Multiplicative Interaction Models Diagnostics and Visualization | 1.2.6 | 1.2.6 |
interleave Converts Tabular Data to Interleaved Vectors | 0.1.2 | 0.1.2 |
interp Interpolation Methods | 1.1-6 | 1.1-6 |
intervals Tools for Working with Points and Intervals | 0.15.4 | 0.15.4 |
IntervalSurgeon Operating on Integer-Bounded Intervals | 1.1 | 1.1 |
intsurv Integrative Survival Modeling | 0.2.2 | 0.2.2 |
inum Interval and Enum-Type Representation of Vectors | 1.0-5 | 1.0-5 |
InvariantCausalPrediction Invariant Causal Prediction | 0.8 | 0.8 |
investr Inverse Estimation/Calibration Functions | 1.4.2 | 1.4.2 |
invgamma The Inverse Gamma Distribution | 1.1 | 1.1 |
invGauss Threshold Regression that Fits the (Randomized Drift) Inverse Gaussian Distribution to Survival Data | 1.2 | 1.2 |
iotools I/O Tools for Streaming | 0.3-5 | 0.3-5 |
ipaddress Data Analysis for IP Addresses and Networks | 1.0.2 | 1.0.2 |
ipcwswitch Inverse Probability of Censoring Weights to Deal with Treatment Switch in Randomized Clinical Trials | 1.0.4 | 1.0.4 |
ipdw Spatial Interpolation by Inverse Path Distance Weighting | 2.0-0 | 2.0-0 |
ipfp Fast Implementation of the Iterative Proportional Fitting Procedure in C | 1.0.2 | 1.0.2 |
ipred Improved Predictors | 0.9-14 | 0.9-14 |
iptools Manipulate, Validate and Resolve 'IP' Addresses | 0.7.2 | 0.7.2 |
ipumsr An R Interface for Downloading, Reading, and Handling IPUMS Data | 0.7.0 | 0.7.0 |
ipw Estimate Inverse Probability Weights | 1.2.1 | 1.2.1 |
IPWboxplot Adapted Boxplot to Missing Observations | 0.1.2 | 0.1.2 |
iqLearn Interactive Q-Learning | 1.5 | 1.5 |
irace Iterated Racing for Automatic Algorithm Configuration | 3.5 | 3.5 |
IRanges | 2.38.1 | 2.38.1 |
IRdisplay 'Jupyter' Display Machinery | 1.1 | 1.1 |
IRkernel Native R Kernel for the 'Jupyter Notebook' | 1.3 | 1.3 |
irlba Fast Truncated Singular Value Decomposition and Principal Components Analysis for Large Dense and Sparse Matrices | 2.3.5.1 | 2.3.5.1 |
irr Various Coefficients of Interrater Reliability and Agreement | 0.84.1 | 0.84.1 |
irrNA Coefficients of Interrater Reliability – Generalized for Randomly Incomplete Datasets | 0.2.3 | 0.2.3 |
irtDemo Item Response Theory Demo Collection | 0.1.4 | 0.1.4 |
irtoys A Collection of Functions Related to Item Response Theory (IRT) | 0.2.2 | 0.2.2 |
irtplay | 1.6.5 | 1.6.5 |
irtrees Estimation of Tree-Based Item Response Models | 1.0.0 | 1.0.0 |
IRTShiny Item Response Theory via Shiny | 1.2 | 1.2 |
Iscores Proper Scoring Rules for Missing Value Imputation | 1.1.0 | 1.1.0 |
isdparser Parse 'NOAA' Integrated Surface Data Files | 0.4.0 | 0.4.0 |
IsingFit Fitting Ising Models Using the ELasso Method | 0.4 | 0.4 |
IsingSampler Sampling Methods and Distribution Functions for the Ising Model | 0.2.3 | 0.2.3 |
islasso The Induced Smoothed Lasso | 1.5.2 | 1.5.2 |
ISLR Data for an Introduction to Statistical Learning with Applications in R | 1.4 | 1.4 |
ismev An Introduction to Statistical Modeling of Extreme Values | 1.42 | 1.42 |
isni Index of Local Sensitivity to Nonignorability | 1.3 | 1.3 |
Iso Functions to Perform Isotonic Regression | 0.0-21 | 0.0-21 |
isoband Generate Isolines and Isobands from Regularly Spaced Elevation Grids | 0.2.6 | 0.2.6 |
ISOcodes Selected ISO Codes | 2023.12.07 | 2023.12.07 |
isopam Clustering of Sites with Species Data | 0.9-13 | 0.9-13 |
IsoSpecR The IsoSpec Algorithm | 2.1.3 | 2.1.3 |
isotone Active Set and Generalized PAVA for Isotone Optimization | 1.1-1 | 1.1-1 |
isotree Isolation-Based Outlier Detection | 0.5.20 | 0.5.20 |
isoWater Discovery, Retrieval, and Analysis of Water Isotope Data | 1.1.2 | 1.1.2 |
ISOweek Week of the year and weekday according to ISO 8601 | 0.6-2 | 0.6-2 |
ISwR Introductory Statistics with R | 2.0-8 | 2.0-8 |
iterators Provides Iterator Construct | 1.0.14 | 1.0.14 |
iterLap Approximate Probability Densities by Iterated Laplace Approximations | 1.1-4 | 1.1-4 |
iterpc Efficient Iterator for Permutations and Combinations | 0.4.2 | 0.4.2 |
itertools Iterator Tools | 0.1-3 | 0.1-3 |
ITRLearn Statistical Learning for Individualized Treatment Regime | 1.0-1 | 1.0-1 |
ITRSelect Variable Selection for Optimal Individualized Dynamic Treatment Regime | 1.0-1 | 1.0-1 |
itscalledsoccer American Soccer Analysis API Client | 0.2.4 | 0.2.4 |
ivfixed Instrumental fixed effect panel data model | 1.0 | 1.0 |
ivmodel Statistical Inference and Sensitivity Analysis for Instrumental Variables Model | 1.9.1 | 1.9.1 |
ivmte Instrumental Variables: Extrapolation by Marginal Treatment Effects | 1.4.0 | 1.4.0 |
ivpack Instrumental Variable Estimation. | 1.2 | 1.2 |
ivpanel Instrumental Panel Data Models | 1.0 | 1.0 |
ivprobit Instrumental Variables Probit Model | 1.1 | 1.1 |
ivreg Instrumental-Variables Regression by '2SLS', '2SM', or '2SMM', with Diagnostics | 0.6-2 | 0.6-2 |
jack Jack, Zonal, Schur, and Other Symmetric Polynomials | 3.0.0 | 3.0.0 |
jackknifeKME Jackknife Estimates of Kaplan-Meier Estimators or Integrals | 1.2 | 1.2 |
JADE Blind Source Separation Methods Based on Joint Diagonalization and Some BSS Performance Criteria | 2.0-4 | 2.0-4 |
jagsUI A Wrapper Around 'rjags' to Streamline 'JAGS' Analyses | 1.6.2 | 1.6.2 |
janeaustenr Jane Austen's Complete Novels | 1.0.0 | 1.0.0 |
janitor Simple Tools for Examining and Cleaning Dirty Data | 2.2.0 | 2.2.0 |
jarbes Just a Rather Bayesian Evidence Synthesis | 2.0.0 | 2.0.0 |
JavaGD Java Graphics Device | 0.6-5 | 0.6-5 |
jjb Balamuta Miscellaneous | 0.1.1 | 0.1.1 |
JM Joint Modeling of Longitudinal and Survival Data | 1.5-2 | 1.5-2 |
JMbayes Joint Modeling of Longitudinal and Time-to-Event Data under a Bayesian Approach | 0.8-85 | 0.8-85 |
JMdesign Joint Modeling of Longitudinal and Survival Data - Power Calculation | 1.5 | 1.5 |
jmvcore Dependencies for the 'jamovi' Framework | 2.4.7 | 2.4.7 |
joineR Joint Modelling of Repeated Measurements and Time-to-Event Data | 1.2.8 | 1.2.8 |
joineRML Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes | 0.4.6 | 0.4.6 |
joinet Penalised Multivariate Regression ('Multi-Target Learning') | 0.0.10 | 0.0.10 |
joint.Cox Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis | 3.16 | 3.16 |
JointAI Joint Analysis and Imputation of Incomplete Data | 1.0.5 | 1.0.5 |
jointDiag Joint Approximate Diagonalization of a Set of Square Matrices | 0.4 | 0.4 |
JointModel Semiparametric Joint Models for Longitudinal and Counting Processes | 1.0 | 1.0 |
jomo Multilevel Joint Modelling Multiple Imputation | 2.7-6 | 2.7-6 |
JoSAE Unit-Level and Area-Level Small Area Estimation | 0.3.0 | 0.3.0 |
jose JavaScript Object Signing and Encryption | 1.2.0 | 1.2.0 |
jpeg Read and write JPEG images | 0.1-10 | 0.1-10 |
jqr Client for 'jq', a 'JSON' Processor | 1.3.3 | 1.3.3 |
jquerylib Obtain 'jQuery' as an HTML Dependency Object | 0.1.4 | 0.1.4 |
jrc Exchange Commands Between R and 'JavaScript' | 0.5.1 | 0.5.1 |
jrt Item Response Theory Modeling and Scoring for Judgment Data | 1.1.2 | 1.1.2 |
js Tools for Working with JavaScript in R | 1.2 | 1.2 |
jsonify Convert Between 'R' Objects and Javascript Object Notation (JSON) | 1.2.2 | 1.2.2 |
jsonld JSON for Linking Data | 2.2 | 2.2 |
jsonlite A Simple and Robust JSON Parser and Generator for R | 1.8.8 | 1.8.8 |
jsonvalidate Validate 'JSON' Schema | 1.3.2 | 1.3.2 |
jstor Read Data from JSTOR/DfR | 0.3.11 | 0.3.11 |
jtools Analysis and Presentation of Social Scientific Data | 2.2.0 | 2.2.0 |
juicr Automated and Manual Extraction of Numerical Data from Scientific Images | 0.1 | 0.1 |
juicyjuice Inline CSS Properties into HTML Tags Using 'juice' | 0.1.0 | 0.1.0 |
JuliaCall Seamless Integration Between R and 'Julia' | 0.17.5 | 0.17.5 |
JuliaConnectoR A Functionally Oriented Interface for Integrating 'Julia' with R | 1.1.3 | 1.1.3 |
JumpeR Importing and Working with Track and Field Data | 0.3.0 | 0.3.0 |
JWileymisc Miscellaneous Utilities and Functions | 1.3.0 | 1.3.0 |
kableExtra Construct Complex Table with 'kable' and Pipe Syntax | 1.4.0 | 1.4.0 |
kalmanfilter Kalman Filter | 2.0.2 | 2.0.2 |
kaos Encoding of Sequences Based on Frequency Matrix Chaos Game Representation | 0.1.2 | 0.1.2 |
kappalab Non-Additive Measure and Integral Manipulation Functions | 0.4-12 | 0.4-12 |
kappaSize Sample Size Estimation Functions for Studies of Interobserver Agreement | 1.2 | 1.2 |
kaps K-Adaptive Partitioning for Survival data | 1.0.2 | 1.0.2 |
kcirt k-Cube Thurstonian IRT Models | 0.6.0 | 0.6.0 |
kdist K-Distribution and Weibull Paper | 0.2 | 0.2 |
kedd Kernel Estimator and Bandwidth Selection for Density and Its Derivatives | 1.0.3 | 1.0.3 |
keep Arrays with Better Control over Dimension Dropping | 1.0 | 1.0 |
KEGGREST | 1.44.1 | 1.44.1 |
kelvin Calculate Solutions to the Kelvin Differential Equation using Bessel Functions | 2.0-2 | 2.0-2 |
Kendall Kendall Rank Correlation and Mann-Kendall Trend Test | 2.2.1 | 2.2.1 |
kendallRandomWalks Simulate and Visualize Kendall Random Walks and Related Distributions | 0.9.4 | 0.9.4 |
KenSyn Knowledge Synthesis in Agriculture - From Experimental Network to Meta-Analysis | 0.3 | 0.3 |
kequate The Kernel Method of Test Equating | 1.6.4 | 1.6.4 |
keras R Interface to 'Keras' | 2.13.0 | 2.13.0 |
keras3 R Interface to 'Keras' | 1.2.0 | 1.2.0 |
kernelboot Smoothed Bootstrap and Random Generation from Kernel Densities | 0.1.10 | 0.1.10 |
Kernelheaping Kernel Density Estimation for Heaped and Rounded Data | 2.3.0 | 2.3.0 |
kernlab Kernel-Based Machine Learning Lab | 0.9-32 | 0.9-32 |
KernSmooth Functions for Kernel Smoothing Supporting Wand & Jones (1995) | 2.23-20 | 2.23-20 |
Keyboard Bayesian Designs for Early Phase Clinical Trials | 0.1.3 | 0.1.3 |
keyring Access the System Credential Store from R | 1.3.0 | 1.3.0 |
KFAS Kalman Filter and Smoother for Exponential Family State Space Models | 1.5.1 | 1.5.1 |
kfigr Integrated Code Chunk Anchoring and Referencing for R Markdown Documents | 1.2.1 | 1.2.1 |
KFKSDS Kalman Filter, Smoother and Disturbance Smoother | 1.6 | 1.6 |
kinship2 Pedigree Functions | 1.9.6 | 1.9.6 |
kitagawa Spectral Response of Water Wells to Harmonic Strain and Pressure Signals | 3.1.2 | 3.1.2 |
kiwisR A Wrapper for Querying KISTERS 'WISKI' Databases via the 'KiWIS' API | 0.2.0 | 0.2.0 |
kknn Weighted k-Nearest Neighbors | 1.3.1 | 1.3.1 |
klaR Classification and Visualization | 1.7-1 | 1.7-1 |
km.ci Confidence Intervals for the Kaplan-Meier Estimator | 0.5-6 | 0.5-6 |
kmc Kaplan-Meier Estimator with Constraints for Right Censored Data -- a Recursive Computational Algorithm | 0.4-2 | 0.4-2 |
kmi Kaplan-Meier Multiple Imputation for the Analysis of Cumulative Incidence Functions in the Competing Risks Setting | 0.5.5 | 0.5.5 |
kml K-Means for Longitudinal Data | 2.4.6.1 | 2.4.6.1 |
KMsurv Data sets from Klein and Moeschberger (1997), Survival Analysis | 0.1-5 | 0.1-5 |
knitcitations Citations for 'Knitr' Markdown Files | 1.0.12 | 1.0.12 |
knitLatex 'Knitr' Helpers - Mostly Tables | 0.9.0 | 0.9.0 |
knitr A General-Purpose Package for Dynamic Report Generation in R | 1.45 | 1.45 |
knn.covertree An Accurate kNN Implementation with Multiple Distance Measures | 1.0 | 1.0 |
kofnGA A Genetic Algorithm for Fixed-Size Subset Selection | 1.3 | 1.3 |
kohonen Supervised and Unsupervised Self-Organising Maps | 3.0.12 | 3.0.12 |
KONPsurv KONP Tests: Powerful K-Sample Tests for Right-Censored Data | 1.0.4 | 1.0.4 |
koRpus Text Analysis with Emphasis on POS Tagging, Readability, and Lexical Diversity | 0.13-8 | 0.13-8 |
KrigInv Kriging-Based Inversion for Deterministic and Noisy Computer Experiments | 1.4.2 | 1.4.2 |
KRIS Keen and Reliable Interface Subroutines for Bioinformatic Analysis | 1.1.6 | 1.1.6 |
ks Kernel Smoothing | 1.14.2 | 1.14.2 |
kSamples K-Sample Rank Tests and their Combinations | 1.2-10 | 1.2-10 |
KScorrect Lilliefors-Corrected Kolmogorov-Smirnov Goodness-of-Fit Tests | 1.4.0 | 1.4.0 |
kst Knowledge Space Theory | 0.5-4 | 0.5-4 |
ktsolve Configurable Function for Solving Families of Nonlinear Equations | 1.3.1 | 1.3.1 |
kutils Project Management Tools | 1.73 | 1.73 |
kwb.hantush Calculation of Groundwater Mounding Beneath an Infiltration Basin | 0.3.0 | 0.3.0 |
kyotil Utility Functions for Statistical Analysis Report Generation and Monte Carlo Studies | 2024.1-30 | 2024.1-30 |
kza Kolmogorov-Zurbenko Adaptive Filters | 4.1.0.1 | 4.1.0.1 |
L0Learn Fast Algorithms for Best Subset Selection | 2.0.3 | 2.0.3 |
labdsv Ordination and Multivariate Analysis for Ecology | 2.1-0 | 2.1-0 |
label.switching Relabelling MCMC Outputs of Mixture Models | 1.8 | 1.8 |
labeling Axis Labeling | 0.4.3 | 0.4.3 |
labelled Manipulating Labelled Data | 2.12.0 | 2.12.0 |
labelVector Label Attributes for Atomic Vectors | 0.1.2 | 0.1.2 |
laeken Estimation of Indicators on Social Exclusion and Poverty | 0.5.3 | 0.5.3 |
LaF Fast Access to Large ASCII Files | 0.8.4 | 0.8.4 |
lagged Classes and Methods for Lagged Objects | 0.3.2 | 0.3.2 |
laGP Local Approximate Gaussian Process Regression | 1.5-9 | 1.5-9 |
Lahman Sean 'Lahman' Baseball Database | 11.0-0 | 11.0-0 |
lakemorpho Lake Morphometry Metrics | 1.3.2 | 1.3.2 |
LAM Some Latent Variable Models | 0.6-19 | 0.6-19 |
lambda.r Modeling Data with Functional Programming | 1.2.4 | 1.2.4 |
LambertW Probabilistic Models to Analyze and Gaussianize Heavy-Tailed, Skewed Data | 0.6.9-1 | 0.6.9-1 |
lamW Lambert-W Function | 2.2.3 | 2.2.3 |
landest Landmark Estimation of Survival and Treatment Effect | 1.2 | 1.2 |
landsat Radiometric and Topographic Correction of Satellite Imagery | 1.1.0 | 1.1.0 |
landscapemetrics Landscape Metrics for Categorical Map Patterns | 2.1.1 | 2.1.1 |
languagelayeR Access the 'languagelayer' API | 1.2.4 | 1.2.4 |
languageserver Language Server Protocol | 0.3.13 | 0.3.13 |
LaplacesDemon Complete Environment for Bayesian Inference | 16.1.6 | 16.1.6 |
LARF Local Average Response Functions for Instrumental Variable Estimation of Treatment Effects | 1.4 | 1.4 |
lars Least Angle Regression, Lasso and Forward Stagewise | 1.3 | 1.3 |
lasso2 L1 Constrained Estimation aka `lasso' | 1.2-22 | 1.2-22 |
lassoshooting L1 Regularized Regression (Lasso) Solver using the Cyclic Coordinate Descent Algorithm aka Lasso Shooting | 0.1.5-1.1 | 0.1.5-1.1 |
latdiag Draws Diagrams Useful for Checking Latent Scales | 0.3 | 0.3 |
latentnet Latent Position and Cluster Models for Statistical Networks | 2.10.6 | 2.10.6 |
later Utilities for Scheduling Functions to Execute Later with Event Loops | 1.3.2 | 1.3.2 |
latex2exp Use LaTeX Expressions in Plots | 0.9.6 | 0.9.6 |
lattice Trellis Graphics for R | 0.22-5 | 0.22-5 |
latticeExtra Extra Graphical Utilities Based on Lattice | 0.6-30 | 0.6-30 |
LatticeKrig Multi-Resolution Kriging Based on Markov Random Fields | 8.4 | 8.4 |
lava Latent Variable Models | 1.7.3 | 1.7.3 |
lavaan Latent Variable Analysis | 0.6-15 | 0.6-15 |
lavaSearch2 Tools for Model Specification in the Latent Variable Framework | 1.5.6 | 1.5.6 |
LAWBL Latent (Variable) Analysis with Bayesian Learning | 1.5.0 | 1.5.0 |
lawstat Tools for Biostatistics, Public Policy, and Law | 3.6 | 3.6 |
lazyeval Lazy (Non-Standard) Evaluation | 0.2.2 | 0.2.2 |
lazyWeave LaTeX Wrappers for R Users | 3.0.2 | 3.0.2 |
lbfgs Limited-memory BFGS Optimization | 1.2.1.2 | 1.2.1.2 |
lbfgsb3c Limited Memory BFGS Minimizer with Bounds on Parameters with optim() 'C' Interface | 2020-3.3 | 2020-3.3 |
lbiassurv Length-biased correction to survival curve estimation. | 1.1 | 1.1 |
LCAvarsel Variable Selection for Latent Class Analysis | 1.1 | 1.1 |
lcmm Extended Mixed Models Using Latent Classes and Latent Processes | 2.1.0 | 2.1.0 |
lcopula Liouville Copulas | 1.0.7 | 1.0.7 |
lctools Local Correlation, Spatial Inequalities, Geographically Weighted Regression and Other Tools | 0.2-10 | 0.2-10 |
lda Collapsed Gibbs Sampling Methods for Topic Models | 1.4.2 | 1.4.2 |
ldat Large Data Sets | 0.3.3 | 0.3.3 |
ldbounds Lan-DeMets Method for Group Sequential Boundaries | 2.0.2 | 2.0.2 |
leafem 'leaflet' Extensions for 'mapview' | 0.2.3 | 0.2.3 |
leafgl High-Performance 'WebGl' Rendering for Package 'leaflet' | 0.1.1 | 0.1.1 |
leaflet Create Interactive Web Maps with the JavaScript 'Leaflet' Library | 2.2.1 | 2.2.1 |
leaflet.extras Extra Functionality for 'leaflet' Package | 1.0.0 | 1.0.0 |
leaflet.extras2 Extra Functionality for 'leaflet' Package | 1.2.0 | 1.2.0 |
leaflet.providers Leaflet Providers | 2.0.0 | 2.0.0 |
leafpm Leaflet Map Plugin for Drawing and Editing | 0.1.0 | 0.1.0 |
leafpop Include Tables, Images and Graphs in Leaflet Pop-Ups | 0.1.0 | 0.1.0 |
leafsync Small Multiples for Leaflet Web Maps | 0.1.0 | 0.1.0 |
leaps Regression Subset Selection | 3.1 | 3.1 |
LearnBayes Functions for Learning Bayesian Inference | 2.15.1 | 2.15.1 |
learnstats An Interactive Environment for Learning Statistics | 0.1.1 | 0.1.1 |
legion Forecasting Using Multivariate Models | 0.1.2 | 0.1.2 |
leiden R Implementation of Leiden Clustering Algorithm | 0.4.3.1 | 0.4.3.1 |
LexisPlotR Plot Lexis Diagrams for Demographic Purposes | 0.4.0 | 0.4.0 |
lexRankr Extractive Summarization of Text with the LexRank Algorithm | 0.5.2 | 0.5.2 |
lfactors Factors with Levels | 1.0.4 | 1.0.4 |
lfe Linear Group Fixed Effects | 2.9-0 | 2.9-0 |
lgarch Simulation and Estimation of Log-GARCH Models | 0.6-2 | 0.6-2 |
lgr A Fully Featured Logging Framework | 0.4.4 | 0.4.4 |
lgtdl A Set of Methods for Longitudinal Data Objects | 1.1.5 | 1.1.5 |
lhs Latin Hypercube Samples | 1.1.5 | 1.1.5 |
libcoin Linear Test Statistics for Permutation Inference | 1.0-10 | 1.0-10 |
libgeos Open Source Geometry Engine ('GEOS') C API | 3.11.1-2 | 3.11.1-2 |
LiblineaR Linear Predictive Models Based on the LIBLINEAR C/C++ Library | 2.10-23 | 2.10-23 |
librarian Install, Update, Load Packages from CRAN, 'GitHub', and 'Bioconductor' in One Step | 1.8.1 | 1.8.1 |
lidR Airborne LiDAR Data Manipulation and Visualization for Forestry Applications | 4.0.4 | 4.0.4 |
lifecontingencies Financial and Actuarial Mathematics for Life Contingencies | 1.3.11 | 1.3.11 |
lifecycle Manage the Life Cycle of your Package Functions | 1.0.4 | 1.0.4 |
liftr Containerize R Markdown Documents for Continuous Reproducibility | 0.9.2 | 0.9.2 |
lightgbm Light Gradient Boosting Machine | 4.3.0 | 4.3.0 |
limma Linear Models for Microarray Data | 3.60.6 | 3.60.6 |
limSolve Solving Linear Inverse Models | 1.5.7 | 1.5.7 |
LindleyPowerSeries Lindley Power Series Distribution | 1.0.1 | 1.0.1 |
linelist Tagging and Validating Epidemiological Data | 1.0.0 | 1.0.0 |
link2GI Linking Geographic Information Systems, Remote Sensing and Other Command Line Tools | 0.5-3 | 0.5-3 |
linpk Generate Concentration-Time Profiles from Linear PK Systems | 1.1.2 | 1.1.2 |
linprog Linear Programming / Optimization | 0.9-4 | 0.9-4 |
LinRegInteractive Interactive Interpretation of Linear Regression Models | 0.3-3 | 0.3-3 |
lintools Manipulation of Linear Systems of (in)Equalities | 0.1.7 | 0.1.7 |
lintr A 'Linter' for R Code | 3.0.0 | 3.0.0 |
lira LInear Regression in Astronomy | 2.0.1 | 2.0.1 |
lisrelToR Import Output from LISREL into R | 0.1.5 | 0.1.5 |
listcomp List Comprehensions | 0.4.1 | 0.4.1 |
listenv Environments Behaving (Almost) as Lists | 0.9.1 | 0.9.1 |
liteq Lightweight Portable Message Queue Using 'SQLite' | 1.1.0 | 1.1.0 |
llogistic The L-Logistic Distribution | 1.0.3 | 1.0.3 |
lme4 Linear Mixed-Effects Models using 'Eigen' and S4 | 1.1-35.1 | 1.1-35.1 |
lmerTest Tests in Linear Mixed Effects Models | 3.1-3 | 3.1-3 |
lmForc Linear Model Forecasting | 0.1.0 | 0.1.0 |
lmm Linear Mixed Models | 1.4 | 1.4 |
lmodel2 Model II Regression | 1.7-3 | 1.7-3 |
lmom L-Moments | 3.0 | 3.0 |
lmomco L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments, and Many Distributions | 2.4.13 | 2.4.13 |
Lmoments L-Moments and Quantile Mixtures | 1.3-1 | 1.3-1 |
lmomRFA Regional Frequency Analysis using L-Moments | 3.6 | 3.6 |
lmtest Testing Linear Regression Models | 0.9-39 | 0.9-39 |
LNIRT LogNormal Response Time Item Response Theory Models | 0.5.1 | 0.5.1 |
lobstr Visualize R Data Structures with Trees | 1.1.2 | 1.1.2 |
localsolver R API to LocalSolver | 2.3 | 2.3 |
locfit Local Regression, Likelihood and Density Estimation | 1.5-9.10 | 1.5-9.10 |
locits Test of Stationarity and Localized Autocovariance | 1.7.7 | 1.7.7 |
locpol Kernel Local Polynomial Regression | 0.8.0 | 0.8.0 |
lodi Limit of Detection Imputation for Single-Pollutant Models | 0.9.2 | 0.9.2 |
loe Local Ordinal Embedding | 1.1 | 1.1 |
logconcens Maximum Likelihood Estimation of a Log-Concave Density Based on Censored Data | 0.17-3 | 0.17-3 |
logcondens Estimate a Log-Concave Probability Density from Iid Observations | 2.1.6 | 2.1.6 |
logger A Lightweight, Modern and Flexible Logging Utility | 0.2.2 | 0.2.2 |
logging R Logging Package | 0.10-108 | 0.10-108 |
LogicReg Logic Regression | 1.6.6 | 1.6.6 |
logitnorm Functions for the Logitnormal Distribution | 0.8.39 | 0.8.39 |
loglognorm Double Log Normal Distribution Functions | 1.0.2 | 1.0.2 |
logOfGamma Natural Logarithms of the Gamma Function for Large Values | 0.0.1 | 0.0.1 |
LogrankA Logrank Test for Aggregated Survival Data | 1.0 | 1.0 |
logspline Routines for Logspline Density Estimation | 2.1.21 | 2.1.21 |
lokern Kernel Regression Smoothing with Local or Global Plug-in Bandwidth | 1.1-10.1 | 1.1-10.1 |
lomb Lomb-Scargle Periodogram | 2.2.0 | 2.2.0 |
longCatEDA Package for Plotting Categorical Longitudinal and Time-Series Data | 0.31 | 0.31 |
longitudinal Analysis of Multiple Time Course Data | 1.1.13 | 1.1.13 |
longitudinalData Longitudinal Data | 2.4.5.1 | 2.4.5.1 |
longmemo Statistics for Long-Memory Processes (Book Jan Beran), and Related Functionality | 1.1-2 | 1.1-2 |
LongMemoryTS Long Memory Time Series | 0.1.0 | 0.1.0 |
longurl Expand Short 'URLs' | 0.3.3 | 0.3.3 |
loo Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models | 2.6.0 | 2.6.0 |
lordif Logistic Ordinal Regression Differential Item Functioning using IRT | 0.3-3 | 0.3-3 |
lori Imputation of High-Dimensional Count Data using Side Information | 2.2.2 | 2.2.2 |
lotri A Simple Way to Specify Symmetric, Block Diagonal Matrices | 0.4.3 | 0.4.3 |
LowRankQP Low Rank Quadratic Programming | 1.0.6 | 1.0.6 |
lpc Lassoed Principal Components for Testing Significance of Features | 1.0.2.1 | 1.0.2.1 |
lpdensity Local Polynomial Density Estimation and Inference | 2.4 | 2.4 |
lpirfs Local Projections Impulse Response Functions | 0.2.3 | 0.2.3 |
LPM Linear Parametric Models Applied to Hydrological Series | 2.9 | 2.9 |
lpSolve Interface to 'Lp_solve' v. 5.5 to Solve Linear/Integer Programs | 5.6.20 | 5.6.20 |
lpSolveAPI R Interface to 'lp_solve' Version 5.5.2.0 | 5.5.2.0-17.11 | 5.5.2.0-17.11 |
LPStimeSeries Learned Pattern Similarity and Representation for Time Series | 1.0-5 | 1.0-5 |
lqa Penalized Likelihood Inference for GLMs | 1.0-3 | 1.0-3 |
lqmm Linear Quantile Mixed Models | 1.5.8 | 1.5.8 |
lsa Latent Semantic Analysis | 0.73.3 | 0.73.3 |
LSD Lots of Superior Depictions | 4.1-0 | 4.1-0 |
lsei Solving Least Squares or Quadratic Programming Problems under Equality/Inequality Constraints | 1.3-0 | 1.3-0 |
lsl Latent Structure Learning | 0.5.6 | 0.5.6 |
lslx Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood or Least Squares | 0.6.11 | 0.6.11 |
lsmeans Least-Squares Means | 2.30-0 | 2.30-0 |
LSMonteCarlo American options pricing with Least Squares Monte Carlo method | 1.0 | 1.0 |
LSMRealOptions Value American and Real Options Through LSM Simulation | 0.2.1 | 0.2.1 |
lspls LS-PLS Models | 0.2-2 | 0.2-2 |
LSTS Locally Stationary Time Series | 2.1 | 2.1 |
LSWPlib Simulation and Spectral Estimation of Locally Stationary Wavelet Packet Processes | 0.1.0 | 0.1.0 |
ltm Latent Trait Models under IRT | 1.2-0 | 1.2-0 |
ltmle Longitudinal Targeted Maximum Likelihood Estimation | 1.3-0 | 1.3-0 |
ltsa Linear Time Series Analysis | 1.4.6 | 1.4.6 |
lubridate Make Dealing with Dates a Little Easier | 1.9.3 | 1.9.3 |
lulcc Land Use Change Modelling in R | 1.0.4 | 1.0.4 |
Luminescence Comprehensive Luminescence Dating Data Analysis | 0.9.23 | 0.9.23 |
lutz Look Up Time Zones of Point Coordinates | 0.3.2 | 0.3.2 |
lvec Out of Memory Vectors | 0.2.5 | 0.2.5 |
lvnet Latent Variable Network Modeling | 0.3.5 | 0.3.5 |
lvplot Letter Value 'Boxplots' | 0.2.1 | 0.2.1 |
LWFBrook90R Simulate Evapotranspiration and Soil Moisture with the SVAT Model LWF-Brook90 | 0.5.3 | 0.5.3 |
lwgeom Bindings to Selected 'liblwgeom' Functions for Simple Features | 0.2-13 | 0.2-13 |
m2b Movement to Behaviour Inference using Random Forest | 1.0 | 1.0 |
m2r Interface to 'Macaulay2' | 1.0.2 | 1.0.2 |
M3 Reading M3 Files | 0.3 | 0.3 |
MAd Meta-Analysis with Mean Differences | 0.8-3 | 0.8-3 |
mada Meta-Analysis of Diagnostic Accuracy | 0.5.10 | 0.5.10 |
maditr Fast Data Aggregation, Modification, and Filtering with Pipes and 'data.table' | 0.8.4 | 0.8.4 |
madrat May All Data be Reproducible and Transparent (MADRaT) * | 3.6.4 | 3.6.4 |
magclass Data Class and Tools for Handling Spatial-Temporal Data | 6.13.2 | 6.13.2 |
magic Create and Investigate Magic Squares | 1.6-1 | 1.6-1 |
magick Advanced Graphics and Image-Processing in R | 2.8.2 | 2.8.2 |
magrittr A Forward-Pipe Operator for R | 2.0.3 | 2.0.3 |
maic Matching-Adjusted Indirect Comparison | 0.1.4 | 0.1.4 |
maicChecks Assessing the Numerical Feasibility for Conducting a Matching-Adjusted Indirect Comparison (MAIC) | 0.1.2 | 0.1.2 |
mailR A Utility to Send Emails from R | 0.8 | 0.8 |
makepipe Pipeline Tools Inspired by 'GNU Make' | 0.2.0 | 0.2.0 |
makeProject Creates an empty package framework for the LCFD format | 1.0 | 1.0 |
MALDIquant Quantitative Analysis of Mass Spectrometry Data | 1.22.2 | 1.22.2 |
MALDIquantForeign Import/Export Routines for 'MALDIquant' | 0.14.1 | 0.14.1 |
MAMS Designing Multi-Arm Multi-Stage Studies | 2.0.1 | 2.0.1 |
manhattanly Interactive Q-Q and Manhattan Plots Using 'plotly.js' | 0.3.0 | 0.3.0 |
ManifoldOptim An R Interface to the 'ROPTLIB' Library for Riemannian Manifold Optimization | 1.0.1 | 1.0.1 |
manipulate Interactive Plots for RStudio | 1.0.1 | 1.0.1 |
manipulateWidget Add Even More Interactivity to Interactive Charts | 0.11.1 | 0.11.1 |
MaOEA Many Objective Evolutionary Algorithm | 0.6.2 | 0.6.2 |
maotai Tools for Matrix Algebra, Optimization and Inference | 0.2.4 | 0.2.4 |
MAPA Multiple Aggregation Prediction Algorithm | 2.0.6 | 2.0.6 |
mapdata Extra Map Databases | 2.3.1 | 2.3.1 |
mapdeck Interactive Maps Using 'Mapbox GL JS' and 'Deck.gl' | 0.3.5 | 0.3.5 |
mapedit Interactive Editing of Spatial Data in R | 0.6.0 | 0.6.0 |
mapiso Create Contour Polygons from Regular Grids | 0.3.0 | 0.3.0 |
maplegend Legends for Maps | 0.1.0 | 0.1.0 |
mapmisc Utilities for Producing Maps | 2.0.3 | 2.0.3 |
mapproj Map Projections | 1.2.11 | 1.2.11 |
maps Draw Geographical Maps | 3.4.2 | 3.4.2 |
mapsapi 'sf'-Compatible Interface to 'Google Maps' APIs | 0.5.3 | 0.5.3 |
mapsf Thematic Cartography | 0.12.0 | 0.12.0 |
mapSpain Administrative Boundaries of Spain | 0.8.0 | 0.8.0 |
mapStats Geographic Display of Survey Data Statistics | 2.4 | 2.4 |
maptiles Download and Display Map Tiles | 0.7.0 | 0.7.0 |
maptools Tools for Handling Spatial Objects | 1.1-8 | 1.1-8 |
maptree Mapping, Pruning, and Graphing Tree Models | 1.4-8 | 1.4-8 |
mapview Interactive Viewing of Spatial Data in R | 2.11.2 | 2.11.2 |
mAr Multivariate AutoRegressive Analysis | 1.2-0 | 1.2-0 |
mar1s Multiplicative AR(1) with Seasonal Processes | 2.1.1 | 2.1.1 |
marcher Migration and Range Change Estimation in R | 0.0-2 | 0.0-2 |
marg Approximate Marginal Inference for Regression-Scale Models | 1.2-2.1 | 1.2-2.1 |
marginaleffects Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests | 0.17.0 | 0.17.0 |
margins Marginal Effects for Model Objects | 0.3.26 | 0.3.26 |
marima Multivariate ARIMA and ARIMA-X Analysis | 2.2 | 2.2 |
markdown Render Markdown with 'commonmark' | 1.12 | 1.12 |
marked Mark-Recapture Analysis for Survival and Abundance Estimation | 1.2.8 | 1.2.8 |
markovchain Easy Handling Discrete Time Markov Chains | 0.9.5 | 0.9.5 |
MarkowitzR Statistical Significance of the Markowitz Portfolio | 1.0.3 | 1.0.3 |
marmap Import, Plot and Analyze Bathymetric and Topographic Data | 1.0.10 | 1.0.10 |
marqLevAlg A Parallelized General-Purpose Optimization Based on Marquardt-Levenberg Algorithm | 2.0.8 | 2.0.8 |
MARSS Multivariate Autoregressive State-Space Modeling | 3.11.8 | 3.11.8 |
MASS Support Functions and Datasets for Venables and Ripley's MASS | 7.3-61 | 7.3-61 |
match2C Match One Sample using Two Criteria | 1.2.4 | 1.2.4 |
Matching Multivariate and Propensity Score Matching with Balance Optimization | 4.10-14 | 4.10-14 |
matchingMarkets Analysis of Stable Matchings | 1.0-4 | 1.0-4 |
matchingR Matching Algorithms in R and C++ | 1.3.3 | 1.3.3 |
MatchIt Nonparametric Preprocessing for Parametric Causal Inference | 4.5.5 | 4.5.5 |
matchMulti Optimal Multilevel Matching using a Network Algorithm | 1.1.12 | 1.1.12 |
MatchThem Matching and Weighting Multiply Imputed Datasets | 1.1.0 | 1.1.0 |
mathjaxr Using 'Mathjax' in Rd Files | 1.6-0 | 1.6-0 |
mathpix Support for the 'Mathpix' API (Image to 'LaTeX') | 0.6.0 | 0.6.0 |
matlab 'MATLAB' Emulation Package | 1.0.4 | 1.0.4 |
matlabr An Interface for MATLAB using System Calls | 1.5.2 | 1.5.2 |
matlib Matrix Functions for Teaching and Learning Linear Algebra and Multivariate Statistics | 0.9.5 | 0.9.5 |
Matrix Sparse and Dense Matrix Classes and Methods | 1.7-1 | 1.7-1 |
matrixcalc Collection of Functions for Matrix Calculations | 1.0-5 | 1.0-5 |
MatrixExtra Extra Methods for Sparse Matrices | 0.1.15 | 0.1.15 |
MatrixGenerics | 1.12.3 | 1.12.3 |
MatrixModels Modelling with Sparse and Dense Matrices | 0.5-3 | 0.5-3 |
matrixNormal The Matrix Normal Distribution | 0.1.1 | 0.1.1 |
matrixsampling Simulations of Matrix Variate Distributions | 2.0.0 | 2.0.0 |
matrixStats Functions that Apply to Rows and Columns of Matrices (and to Vectors) | 1.2.0 | 1.2.0 |
maxcombo The Group Sequential Max-Combo Test for Comparing Survival Curves | 1.0 | 1.0 |
maxLik Maximum Likelihood Estimation and Related Tools | 1.5-2 | 1.5-2 |
MaxPro Maximum Projection Designs | 4.1-2 | 4.1-2 |
maxstat Maximally Selected Rank Statistics | 0.7-25 | 0.7-25 |
MBA Multilevel B-Spline Approximation | 0.1-0 | 0.1-0 |
mbbefd Maxwell Boltzmann Bose Einstein Fermi Dirac Distribution and Destruction Rate Modelling | 0.8.11 | 0.8.11 |
MBC Multivariate Bias Correction of Climate Model Outputs | 0.10-6 | 0.10-6 |
mbend Matrix Bending | 1.3.1 | 1.3.1 |
MBESS The MBESS R Package | 4.9.3 | 4.9.3 |
MBHdesign Spatial Designs for Ecological and Environmental Surveys | 2.3.15 | 2.3.15 |
mblm Median-Based Linear Models | 0.12.1 | 0.12.1 |
MBNMAdose Dose-Response MBNMA Models | 0.4.2 | 0.4.2 |
MBNMAtime Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models | 0.2.4 | 0.2.4 |
mboost Model-Based Boosting | 2.9-11 | 2.9-11 |
MBSP Multivariate Bayesian Model with Shrinkage Priors | 4.0 | 4.0 |
mbsts Multivariate Bayesian Structural Time Series | 3.0 | 3.0 |
mc.heterogeneity A Monte Carlo Based Heterogeneity Test for Meta-Analysis | 0.1.2 | 0.1.2 |
mc2d Tools for Two-Dimensional Monte-Carlo Simulations | 0.2.0 | 0.2.0 |
MCAvariants Multiple Correspondence Analysis Variants | 2.6.1 | 2.6.1 |
mcclust Process an MCMC Sample of Clusterings | 1.0.1 | 1.0.1 |
mcga Machine Coded Genetic Algorithms for Real-Valued Optimization Problems | 3.0.7 | 3.0.7 |
mclcar Estimating Conditional Auto-Regressive (CAR) Models using Monte Carlo Likelihood Methods | 0.2-0 | 0.2-0 |
mclogit Multinomial Logit Models, with or without Random Effects or Overdispersion | 0.9.6 | 0.9.6 |
mclust Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation | 6.0.1 | 6.0.1 |
mclustcomp Measures for Comparing Clusters | 0.3.3 | 0.3.3 |
mcmc Markov Chain Monte Carlo | 0.9-8 | 0.9-8 |
MCMC4Extremes Posterior Distribution of Extreme Value Models in R | 1.1 | 1.1 |
mcmcensemble Ensemble Sampler for Affine-Invariant MCMC | 3.0.0 | 3.0.0 |
MCMCglmm MCMC Generalised Linear Mixed Models | 2.35 | 2.35 |
MCMCpack Markov Chain Monte Carlo (MCMC) Package | 1.7-0 | 1.7-0 |
mcmcplots Create Plots from MCMC Output | 0.4.3 | 0.4.3 |
mcmcr Manipulate MCMC Samples | 0.6.1 | 0.6.1 |
mcmcse Monte Carlo Standard Errors for MCMC | 1.5-0 | 1.5-0 |
MCMCvis Tools to Visualize, Manipulate, and Summarize MCMC Output | 0.16.3 | 0.16.3 |
mco Multiple Criteria Optimization Algorithms and Related Functions | 1.16 | 1.16 |
Mcomp Data from the M-Competitions | 2.8 | 2.8 |
mcompanion Objects and Methods for Multi-Companion Matrices | 0.6 | 0.6 |
MCPMod Design and Analysis of Dose-Finding Studies | 1.0-10.1 | 1.0-10.1 |
mda Mixture and Flexible Discriminant Analysis | 0.5-4 | 0.5-4 |
mde Missing Data Explorer | 0.3.2 | 0.3.2 |
mded Measuring the Difference Between Two Empirical Distributions | 0.1-2 | 0.1-2 |
mdendro Extended Agglomerative Hierarchical Clustering | 2.2.1 | 2.2.1 |
mdftracks Read and Write 'MTrackJ Data Files' | 0.2.2 | 0.2.2 |
mdgc Missing Data Imputation Using Gaussian Copulas | 0.1.7 | 0.1.7 |
mdmb Model Based Treatment of Missing Data | 1.8-7 | 1.8-7 |
measurementProtocol Send Data from R to the Measurement Protocol | 0.1.1 | 0.1.1 |
measurements Tools for Units of Measurement | 1.5.1 | 1.5.1 |
measures Performance Measures for Statistical Learning | 0.3 | 0.3 |
meboot Maximum Entropy Bootstrap for Time Series | 1.4-9.4 | 1.4-9.4 |
medAdherence Medication Adherence: Commonly Used Definitions | 1.03 | 1.03 |
medflex Flexible Mediation Analysis Using Natural Effect Models | 0.6-10 | 0.6-10 |
Mediana Clinical Trial Simulations | 1.0.8 | 1.0.8 |
mediation Causal Mediation Analysis | 4.5.0 | 4.5.0 |
MedSurvey Linear Mediation Analysis for Complex Surveys Using Balanced Repeated Replication | 1.1.1.3.0 | 1.1.1.3.0 |
mefa Multivariate Data Handling in Ecology and Biogeography | 3.2-8 | 3.2-8 |
mem The Moving Epidemic Method | 2.18 | 2.18 |
memapp The Moving Epidemic Method Web Application | 2.16 | 2.16 |
memisc Management of Survey Data and Presentation of Analysis Results | 0.99.31.7 | 0.99.31.7 |
memoise 'Memoisation' of Functions | 2.0.1 | 2.0.1 |
memuse Memory Estimation Utilities | 4.2-3 | 4.2-3 |
MendelianRandomization Mendelian Randomization Package | 0.10.0 | 0.10.0 |
MEPDF Creation of Empirical Density Functions Based on Multivariate Data | 3.0 | 3.0 |
merTools Tools for Analyzing Mixed Effect Regression Models | 0.6.1 | 0.6.1 |
MESS Miscellaneous Esoteric Statistical Scripts | 0.5.12 | 0.5.12 |
meta General Package for Meta-Analysis | 7.0-0 | 7.0-0 |
meta.shrinkage Meta-Analyses for Simultaneously Estimating Individual Means | 0.1.4 | 0.1.4 |
meta4diag Meta-Analysis for Diagnostic Test Studies | 2.1.1 | 2.1.1 |
MetaAnalyser An Interactive Visualisation of Meta-Analysis as a Physical Weighing Machine | 0.2.1 | 0.2.1 |
MetABEL Meta-analysis of genome-wide SNP association results | 0.2-0 | 0.2-0 |
metabias Meta-Analysis for Within-Study and/or Across-Study Biases | 0.1.1 | 0.1.1 |
metaBLUE BLUE for Combining Location and Scale Information in a Meta-Analysis | 1.0.0 | 1.0.0 |
metaBMA Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis | 0.6.9 | 0.6.9 |
MetabolAnalyze Probabilistic latent variable models for metabolomic data. | 1.3.1 | 1.3.1 |
metabolic Datasets and Functions for Reproducing Meta-Analyses | 0.1.2 | 0.1.2 |
metacart Meta-CART: A Flexible Approach to Identify Moderators in Meta-Analysis | 2.0-3 | 2.0-3 |
metacom Analysis of the 'Elements of Metacommunity Structure' | 1.5.3 | 1.5.3 |
metaconfoundr Visualize 'Confounder' Control in Meta-Analyses | 0.1.2 | 0.1.2 |
metacor Meta-Analysis of Correlation Coefficients | 1.0-2.1 | 1.0-2.1 |
metadat Meta-Analysis Datasets | 1.2-0 | 1.2-0 |
metaDigitise Extract and Summarise Data from Published Figures | 1.0.1 | 1.0.1 |
metafor Meta-Analysis Package for R | 4.4-0 | 4.4-0 |
metaforest Exploring Heterogeneity in Meta-Analysis using Random Forests | 0.1.4 | 0.1.4 |
metafuse Fused Lasso Approach in Regression Coefficient Clustering | 2.0-1 | 2.0-1 |
metagam Meta-Analysis of Generalized Additive Models | 0.4.0 | 0.4.0 |
metagear Comprehensive Research Synthesis Tools for Systematic Reviews and Meta-Analysis | 0.7 | 0.7 |
metaheuristicOpt Metaheuristic for Optimization | 2.0.0 | 2.0.0 |
MetaIntegration Ensemble Meta-Prediction Framework | 0.1.2 | 0.1.2 |
metaLik Likelihood Inference in Meta-Analysis and Meta-Regression Models | 0.43.0 | 0.43.0 |
metamedian Meta-Analysis of Medians | 1.1.1 | 1.1.1 |
metamicrobiomeR Microbiome Data Analysis & Meta-Analysis with GAMLSS-BEZI & Random Effects | 1.2 | 1.2 |
metamisc Meta-Analysis of Diagnosis and Prognosis Research Studies | 0.4.0 | 0.4.0 |
metaMix Bayesian Mixture Analysis for Metagenomic Community Profiling | 0.3 | 0.3 |
metansue Meta-Analysis of Studies with Non-Statistically Significant Unreported Effects | 2.5 | 2.5 |
metapack Bayesian Meta-Analysis and Network Meta-Analysis | 0.3 | 0.3 |
metaplus Robust Meta-Analysis and Meta-Regression | 1.0-4 | 1.0-4 |
metapod | 1.12.0 | 1.12.0 |
metapower Power Analysis for Meta-Analysis | 0.2.2 | 0.2.2 |
metarep Replicability-Analysis Tools for Meta-Analysis | 1.2.0 | 1.2.0 |
metaRMST Meta-Analysis of RMSTD | 1.0.0 | 1.0.0 |
metaRNASeq Meta-Analysis of RNA-Seq Data | 1.0.7 | 1.0.7 |
metaSEM Meta-Analysis using Structural Equation Modeling | 1.3.1 | 1.3.1 |
metasens Statistical Methods for Sensitivity Analysis in Meta-Analysis | 1.5-2 | 1.5-2 |
MetaStan Bayesian Meta-Analysis via 'Stan' | 1.0.0 | 1.0.0 |
MetaSubtract Subtracting Summary Statistics of One or more Cohorts from Meta-GWAS Results | 1.60 | 1.60 |
metaSurvival Meta-Analysis of a Single Survival Curve | 0.1.0 | 0.1.0 |
metatest Fit and Test Metaregression Models | 1.0-5 | 1.0-5 |
Metatron Meta-analysis for Classification Data and Correction to Imperfect Reference | 0.1-1 | 0.1-1 |
metaumbrella Umbrella Review Package for R | 1.0.11 | 1.0.11 |
MetaUtility Utility Functions for Conducting and Interpreting Meta-Analyses | 2.1.2 | 2.1.2 |
metavcov Computing Variances and Covariances, Visualization and Missing Data Solution for Multivariate Meta-Analysis | 2.1.5 | 2.1.5 |
metaviz Forest Plots, Funnel Plots, and Visual Funnel Plot Inference for Meta-Analysis | 0.3.1 | 0.3.1 |
metawho Meta-Analytical Implementation to Identify Who Benefits Most from Treatments | 0.2.0 | 0.2.0 |
meteo RFSI & STRK Interpolation for Meteo and Environmental Variables | 2.0-2 | 2.0-2 |
meteoland Landscape Meteorology Tools | 2.2.1 | 2.2.1 |
meteospain Access to Spanish Meteorological Stations Services | 0.1.2 | 0.1.2 |
methods | 4.4.1 | 4.4.1 |
metR Tools for Easier Analysis of Meteorological Fields | 0.14.1 | 0.14.1 |
Metrics Evaluation Metrics for Machine Learning | 0.1.4 | 0.1.4 |
metRology Support for Metrological Applications | 0.9-28-1 | 0.9-28-1 |
mets Analysis of Multivariate Event Times | 1.3.3 | 1.3.3 |
mev Modelling of Extreme Values | 1.16 | 1.16 |
mexhaz Mixed Effect Excess Hazard Models | 2.4 | 2.4 |
mfaces Fast Covariance Estimation for Multivariate Sparse Functional Data | 0.1-3 | 0.1-3 |
mfbvar Mixed-Frequency Bayesian VAR Models | 0.5.6 | 0.5.6 |
mFilter Miscellaneous Time Series Filters | 0.1-5 | 0.1-5 |
MFPCA Multivariate Functional Principal Component Analysis for Data Observed on Different Dimensional Domains | 1.3-10 | 1.3-10 |
mfx Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs | 1.2-2 | 1.2-2 |
mgcv Mixed GAM Computation Vehicle with Automatic Smoothness Estimation | 1.9-1 | 1.9-1 |
mgcViz Visualisations for Generalized Additive Models | 0.1.11 | 0.1.11 |
mgm Estimating Time-Varying k-Order Mixed Graphical Models | 1.2-14 | 1.2-14 |
MGMM Missingness Aware Gaussian Mixture Models | 1.0.1.1 | 1.0.1.1 |
mgsub Safe, Multiple, Simultaneous String Substitution | 1.7.3 | 1.7.3 |
mhsmm Inference for Hidden Markov and Semi-Markov Models | 0.4.21 | 0.4.21 |
mhurdle Multiple Hurdle Tobit Models | 1.3-0 | 1.3-0 |
mi Missing Data Imputation and Model Checking | 1.1 | 1.1 |
mice Multivariate Imputation by Chained Equations | 3.16.0 | 3.16.0 |
miceadds Some Additional Multiple Imputation Functions, Especially for 'mice' | 3.17-44 | 3.17-44 |
micEcon Microeconomic Analysis and Modelling | 0.6-18 | 0.6-18 |
micEconAids Demand Analysis with the Almost Ideal Demand System (AIDS) | 0.6-20 | 0.6-20 |
micEconCES Analysis with the Constant Elasticity of Substitution (CES) Function | 1.0-2 | 1.0-2 |
micEconIndex Price and Quantity Indices | 0.1-8 | 0.1-8 |
micEconSNQP Symmetric Normalized Quadratic Profit Function | 0.6-10 | 0.6-10 |
miceFast Fast Imputations Using 'Rcpp' and 'Armadillo' | 0.8.2 | 0.8.2 |
micemd Multiple Imputation by Chained Equations with Multilevel Data | 1.10.0 | 1.10.0 |
miceRanger Multiple Imputation by Chained Equations with Random Forests | 1.5.0 | 1.5.0 |
miCoPTCM Promotion Time Cure Model with Mis-Measured Covariates | 1.1 | 1.1 |
microbenchmark Accurate Timing Functions | 1.4.10 | 1.4.10 |
micromap Linked Micromap Plots | 1.9.7 | 1.9.7 |
microsamplingDesign Finding Optimal Microsampling Designs for Non-Compartmental Pharmacokinetic Analysis | 1.0.8 | 1.0.8 |
Microsoft365R Interface to the 'Microsoft 365' Suite of Cloud Services | 2.4.0 | 2.4.0 |
microsynth Synthetic Control Methods with Micro- And Meso-Level Data | 2.0.44 | 2.0.44 |
MicSim Performing Continuous-Time Microsimulation | 2.0.1 | 2.0.1 |
midasr Mixed Data Sampling Regression | 0.8 | 0.8 |
MIIVsem Model Implied Instrumental Variable (MIIV) Estimation of Structural Equation Models | 0.5.8 | 0.5.8 |
mime Map Filenames to MIME Types | 0.12 | 0.12 |
mimi Main Effects and Interactions in Mixed and Incomplete Data | 0.2.0 | 0.2.0 |
mind Multivariate Model Based Inference for Domains | 1.1.0 | 1.1.0 |
MinEDfind A Bayesian Design for Minimum Effective Dosing-Finding Trial | 0.1.3 | 0.1.3 |
minerva Maximal Information-Based Nonparametric Exploration for Variable Analysis | 1.5.10 | 1.5.10 |
miniCRAN Create a Mini Version of CRAN Containing Only Selected Packages | 0.2.16 | 0.2.16 |
minimalRSD Minimally Changed CCD and BBD | 1.0.0 | 1.0.0 |
minimax The Minimax Distribution Family | 1.1.1 | 1.1.1 |
minimaxdesign Minimax and Minimax Projection Designs | 0.1.5 | 0.1.5 |
miniUI Shiny UI Widgets for Small Screens | 0.1.1.1 | 0.1.1.1 |
minpack.lm R Interface to the Levenberg-Marquardt Nonlinear Least-Squares Algorithm Found in MINPACK, Plus Support for Bounds | 1.2-4 | 1.2-4 |
minqa Derivative-Free Optimization Algorithms by Quadratic Approximation | 1.2.6 | 1.2.6 |
minty Minimal Type Guesser | 0.0.1 | 0.0.1 |
mipfp Multidimensional Iterative Proportional Fitting and Alternative Models | 3.2.1 | 3.2.1 |
mirai Minimalist Async Evaluation Framework for R | 0.12.0 | 0.12.0 |
mirt Multidimensional Item Response Theory | 1.41 | 1.41 |
mirtCAT Computerized Adaptive Testing with Multidimensional Item Response Theory | 1.13 | 1.13 |
misaem Linear Regression and Logistic Regression with Missing Covariates | 1.0.1 | 1.0.1 |
misc3d Miscellaneous 3D Plots | 0.9-1 | 0.9-1 |
miscF Miscellaneous Functions | 0.1-5 | 0.1-5 |
miscTools Miscellaneous Tools and Utilities | 0.6-28 | 0.6-28 |
missForest Nonparametric Missing Value Imputation using Random Forest | 1.5 | 1.5 |
missingHE Missing Outcome Data in Health Economic Evaluation | 1.5.0 | 1.5.0 |
missMDA Handling Missing Values with Multivariate Data Analysis | 1.19 | 1.19 |
missMethods Methods for Missing Data | 0.4.0 | 0.4.0 |
missRanger Fast Imputation of Missing Values | 2.4.0 | 2.4.0 |
missSBM Handling Missing Data in Stochastic Block Models | 1.0.4 | 1.0.4 |
misty Miscellaneous Functions 'T. Yanagida' | 0.6.0 | 0.6.0 |
mitml Tools for Multiple Imputation in Multilevel Modeling | 0.4-5 | 0.4-5 |
mitools Tools for Multiple Imputation of Missing Data | 2.4 | 2.4 |
MittagLeffleR Mittag-Leffler Family of Distributions | 0.4.1 | 0.4.1 |
miWQS Multiple Imputation Using Weighted Quantile Sum Regression | 0.4.4 | 0.4.4 |
mix Estimation/Multiple Imputation for Mixed Categorical and Continuous Data | 1.0-11 | 1.0-11 |
mixAK Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering | 5.7 | 5.7 |
MixAll Clustering and Classification using Model-Based Mixture Models | 1.5.1 | 1.5.1 |
mixAR Mixture Autoregressive Models | 0.22.8 | 0.22.8 |
mixdist Finite Mixture Distribution Models | 0.5-5 | 0.5-5 |
MixedTS Mixed Tempered Stable Distribution | 1.0.4 | 1.0.4 |
mixexp Design and Analysis of Mixture Experiments | 1.2.5 | 1.2.5 |
MixMatrix Classification with Matrix Variate Normal and t Distributions | 0.2.6 | 0.2.6 |
mixmeta An Extended Mixed-Effects Framework for Meta-Analysis | 1.2.0 | 1.2.0 |
mixPHM Mixtures of Proportional Hazard Models | 0.7-2 | 0.7-2 |
mixR Finite Mixture Modeling for Raw and Binned Data | 0.2.0 | 0.2.0 |
mixRasch Mixture Rasch Models with JMLE | 1.1 | 1.1 |
mixreg Functions to Fit Mixtures of Regressions | 2.0-10 | 2.0-10 |
MixSim Simulating Data to Study Performance of Clustering Algorithms | 1.1-7 | 1.1-7 |
mixsmsn Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions | 1.1-10 | 1.1-10 |
mixSPE Mixtures of Power Exponential and Skew Power Exponential Distributions for Use in Model-Based Clustering and Classification | 0.9.2 | 0.9.2 |
mixsqp Sequential Quadratic Programming for Fast Maximum-Likelihood Estimation of Mixture Proportions | 0.3-54 | 0.3-54 |
mixtools Tools for Analyzing Finite Mixture Models | 1.2.0 | 1.2.0 |
mixture Mixture Models for Clustering and Classification | 2.1.1 | 2.1.1 |
mize Unconstrained Numerical Optimization Algorithms | 0.2.4 | 0.2.4 |
mkde 2D and 3D Movement-Based Kernel Density Estimates (MKDEs) | 0.3 | 0.3 |
MKdescr Descriptive Statistics | 0.8 | 0.8 |
mkin Kinetic Evaluation of Chemical Degradation Data | 1.2.6 | 1.2.6 |
MKinfer Inferential Statistics | 1.1 | 1.1 |
mknapsack Multiple Knapsack Problem Solver | 0.1.0 | 0.1.0 |
mkssd Efficient Multi-Level k-Circulant Supersaturated Designs | 1.2 | 1.2 |
mlapi Abstract Classes for Building 'scikit-learn' Like API | 0.1.1 | 0.1.1 |
mlbench Machine Learning Benchmark Problems | 2.1-3.1 | 2.1-3.1 |
mlbstats Major League Baseball Player Statistics Calculator | 0.1.0 | 0.1.0 |
MLCIRTwithin Latent Class Item Response Theory (LC-IRT) Models under Within-Item Multidimensionality | 2.1.1 | 2.1.1 |
MLDS Maximum Likelihood Difference Scaling | 0.5.1 | 0.5.1 |
MLEcens Computation of the MLE for Bivariate Interval Censored Data | 0.1-7 | 0.1-7 |
MLmetrics Machine Learning Evaluation Metrics | 1.1.1 | 1.1.1 |
mlmi Maximum Likelihood Multiple Imputation | 1.1.2 | 1.1.2 |
mlogit Multinomial Logit Models | 1.1-1 | 1.1-1 |
mlogitBMA Bayesian Model Averaging for Multinomial Logit Models | 0.1-7 | 0.1-7 |
mlpack 'Rcpp' Integration for the 'mlpack' Library | 4.3.0 | 4.3.0 |
mlr Machine Learning in R | 2.19.1 | 2.19.1 |
mlr3 Machine Learning in R - Next Generation | 0.17.2 | 0.17.2 |
mlr3learners Recommended Learners for 'mlr3' | 0.5.8 | 0.5.8 |
mlr3measures Performance Measures for 'mlr3' | 0.5.0 | 0.5.0 |
mlr3misc Helper Functions for 'mlr3' | 0.13.0 | 0.13.0 |
mlr3pipelines Preprocessing Operators and Pipelines for 'mlr3' | 0.5.0-2 | 0.5.0-2 |
mlr3spatiotempcv Spatiotemporal Resampling Methods for 'mlr3' | 2.3.0 | 2.3.0 |
mlr3tuning Hyperparameter Optimization for 'mlr3' | 0.19.2 | 0.19.2 |
mlrMBO Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions | 1.1.5.1 | 1.1.5.1 |
mlt Most Likely Transformations | 1.4-9 | 1.4-9 |
mltools Machine Learning Tools | 0.3.5 | 0.3.5 |
mlVAR Multi-Level Vector Autoregression | 0.5.1 | 0.5.1 |
MM The Multiplicative Multinomial Distribution | 1.6-7 | 1.6-7 |
mma Multiple Mediation Analysis | 10.7-1 | 10.7-1 |
mmand Mathematical Morphology in Any Number of Dimensions | 1.6.3 | 1.6.3 |
mmap Map Pages of Memory | 0.6-21 | 0.6-21 |
MMDai Multivariate Multinomial Distribution Approximation and Imputation for Incomplete Categorical Data | 2.0.0 | 2.0.0 |
mmeta Multivariate Meta-Analysis | 3.0.0 | 3.0.0 |
MNB Diagnostic Tools for a Multivariate Negative Binomial Model | 1.1.0 | 1.1.0 |
mniw The Matrix-Normal Inverse-Wishart Distribution | 1.0.1 | 1.0.1 |
mnormt The Multivariate Normal and t Distributions, and Their Truncated Versions | 2.1.1 | 2.1.1 |
MNP Fitting the Multinomial Probit Model | 3.1-4 | 3.1-4 |
MOCCA Multi-Objective Optimization for Collecting Cluster Alternatives | 1.4 | 1.4 |
mockery Mocking Library for R | 0.4.3 | 0.4.3 |
modeest Mode Estimation | 2.4.0 | 2.4.0 |
model4you Stratified and Personalised Models Based on Model-Based Trees and Forests | 0.9-7 | 0.9-7 |
modelbased Estimation of Model-Based Predictions, Contrasts and Means | 0.8.6 | 0.8.6 |
modeldata Data Sets Useful for Modeling Examples | 1.3.0 | 1.3.0 |
modelenv Provide Tools to Register Models for Use in 'tidymodels' | 0.1.1 | 0.1.1 |
ModelMap Modeling and Map Production using Random Forest and Related Stochastic Models | 3.4.0.4 | 3.4.0.4 |
ModelMetrics Rapid Calculation of Model Metrics | 1.2.2.2 | 1.2.2.2 |
modelObj A Model Object Framework for Regression Analysis | 4.2 | 4.2 |
modelr Modelling Functions that Work with the Pipe | 0.1.11 | 0.1.11 |
modelsummary Summary Tables and Plots for Statistical Models and Data: Beautiful, Customizable, and Publication-Ready | 1.4.3 | 1.4.3 |
modeltools Tools and Classes for Statistical Models | 0.2-23 | 0.2-23 |
moderndive Tidyverse-Friendly Introductory Linear Regression | 0.5.5 | 0.5.5 |
MODIS Acquisition and Processing of MODIS Products | 1.2.3.9008 | 1.2.3.9008 |
MODISTools Interface to the 'MODIS Land Products Subsets' Web Services | 1.1.5 | 1.1.5 |
modules Self Contained Units of Source Code | 0.12.0 | 0.12.0 |
MoEClust Gaussian Parsimonious Clustering Models with Covariates and a Noise Component | 1.5.2 | 1.5.2 |
mokken Conducts Mokken Scale Analysis | 3.0.6 | 3.0.6 |
mombf Model Selection with Bayesian Methods and Information Criteria | 3.5.2 | 3.5.2 |
moments Moments, Cumulants, Skewness, Kurtosis and Related Tests | 0.14.1 | 0.14.1 |
MomTrunc Moments of Folded and Doubly Truncated Multivariate Distributions | 6.0 | 6.0 |
mondate Keep Track of Dates in Terms of Months | 0.10.02 | 0.10.02 |
mongolite Fast and Simple 'MongoDB' Client for R | 2.7.3 | 2.7.3 |
monmlp Multi-Layer Perceptron Neural Network with Optional Monotonicity Constraints | 1.1.5 | 1.1.5 |
monobin Monotonic Binning for Credit Rating Models | 0.2.3 | 0.2.3 |
monomvn Estimation for MVN and Student-t Data with Monotone Missingness | 1.9-20 | 1.9-20 |
MonoPoly Functions to Fit Monotone Polynomials | 0.3-10 | 0.3-10 |
moonBook Functions and Datasets for the Book by Keon-Woong Moon | 0.3.1 | 0.3.1 |
Morpho Calculations and Visualisations Related to Geometric Morphometrics | 2.12 | 2.12 |
mosaic Project MOSAIC Statistics and Mathematics Teaching Utilities | 1.9.0 | 1.9.0 |
mosaicCore Common Utilities for Other MOSAIC-Family Packages | 0.9.4.0 | 0.9.4.0 |
mosaicData Project MOSAIC Data Sets | 0.20.4 | 0.20.4 |
MOTE Effect Size and Confidence Interval Calculator | 1.0.2 | 1.0.2 |
mousetrack Mouse-Tracking Measures from Trajectory Data | 1.0.0 | 1.0.0 |
mousetrap Process and Analyze Mouse-Tracking Data | 3.2.3 | 3.2.3 |
move Visualizing and Analyzing Animal Track Data | 4.2.4 | 4.2.4 |
move2 Processing and Analysing Animal Trajectories | 0.3.0 | 0.3.0 |
movecost Calculation of Slope-Dependant Accumulated Cost Surface, Least-Cost Paths, Least-Cost Corridors, Least-Cost Networks Related to Human Movement Across the Landscape | 2.0 | 2.0 |
moveHMM Animal Movement Modelling using Hidden Markov Models | 1.9 | 1.9 |
moveWindSpeed Estimate Wind Speeds from Bird Trajectories | 0.2.4 | 0.2.4 |
movMF Mixtures of von Mises-Fisher Distributions | 0.2-8 | 0.2-8 |
mpath Regularized Linear Models | 0.4-2.23 | 0.4-2.23 |
MPDiR Data Sets and Scripts for Modeling Psychophysical Data in R | 0.2 | 0.2 |
MplusAutomation An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus | 1.1.1 | 1.1.1 |
mpmi Mixed-Pair Mutual Information Estimators | 0.43.2.1 | 0.43.2.1 |
MPO.db | 0.99.7 | 0.99.7 |
mpoly Symbolic Computation and More with Multivariate Polynomials | 1.1.1 | 1.1.1 |
MPS Estimating Through the Maximum Product Spacing Approach | 2.3.1 | 2.3.1 |
mpt Multinomial Processing Tree Models | 0.8-0 | 0.8-0 |
MPTinR Analyze Multinomial Processing Tree Models | 1.14.1 | 1.14.1 |
MPV Data Sets from Montgomery, Peck and Vining | 1.63 | 1.63 |
mQTL Metabolomic Quantitative Trait Locus Mapping | 1.0 | 1.0 |
mr.raps Two Sample Mendelian Randomization using Robust Adjusted Profile Score | 0.2 | 0.2 |
mrds Mark-Recapture Distance Sampling | 2.3.0 | 2.3.0 |
mrf Multiresolution Forecasting | 0.1.6 | 0.1.6 |
mrfDepth Depth Measures in Multivariate, Regression and Functional Settings | 1.0.16 | 1.0.16 |
mrgsolve Simulate from ODE-Based Models | 1.3.0 | 1.3.0 |
mritc MRI Tissue Classification | 0.5-3 | 0.5-3 |
mRMRe Parallelized Minimum Redundancy, Maximum Relevance (mRMR) | 2.1.2.1 | 2.1.2.1 |
mschart Chart Generation for 'Microsoft Word' and 'Microsoft PowerPoint' Documents | 0.4.0 | 0.4.0 |
mscstexta4r R Client for the Microsoft Cognitive Services Text Analytics REST API | 0.1.2 | 0.1.2 |
mscsweblm4r R Client for the Microsoft Cognitive Services Web Language Model REST API | 0.1.2 | 0.1.2 |
MSGARCH Markov-Switching GARCH Models | 2.51 | 2.51 |
msImpute | 1.14.0 | 1.14.0 |
msm Multi-State Markov and Hidden Markov Models in Continuous Time | 1.7.1 | 1.7.1 |
msmtools Building Augmented Data to Run Multi-State Models with 'msm' Package | 2.0.1 | 2.0.1 |
msos Data Sets and Functions Used in Multivariate Statistics: Old School by John Marden | 1.2.0 | 1.2.0 |
mssm Multivariate State Space Models | 0.1.6 | 0.1.6 |
MSSQL Tools to Work with Microsoft SQL Server Databases via 'RODBC' | 1.0.0 | 1.0.0 |
MST Multivariate Survival Trees | 2.2 | 2.2 |
mstate Data Preparation, Estimation and Prediction in Multi-State Models | 0.3.2 | 0.3.2 |
MSwM Fitting Markov Switching Models | 1.5 | 1.5 |
MTS All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models | 1.2.1 | 1.2.1 |
mtsdi Multivariate Time Series Data Imputation | 0.3.5 | 0.3.5 |
mudfold Multiple UniDimensional unFOLDing | 1.1.21 | 1.1.21 |
muhaz Hazard Function Estimation in Survival Analysis | 1.2.6.4 | 1.2.6.4 |
multcomp Simultaneous Inference in General Parametric Models | 1.4-25 | 1.4-25 |
multcompView Visualizations of Paired Comparisons | 0.1-10 | 0.1-10 |
multDM Multivariate Version of the Diebold-Mariano Test | 1.1.4 | 1.1.4 |
multicool Permutations of Multisets in Cool-Lex Order | 1.0.0 | 1.0.0 |
multifamm Multivariate Functional Additive Mixed Models | 0.1.1 | 0.1.1 |
MultiFit Multiscale Fisher's Independence Test for Multivariate Dependence | 1.1.1 | 1.1.1 |
MultiLCIRT Multidimensional Latent Class Item Response Theory Models | 2.11 | 2.11 |
multilevel Multilevel Functions | 2.6 | 2.6 |
multinma Bayesian Network Meta-Analysis of Individual and Aggregate Data | 0.5.1 | 0.5.1 |
MultipleBubbles Test and Detection of Explosive Behaviors for Time Series | 0.2.0 | 0.2.0 |
multipleNCC Weighted Cox-Regression for Nested Case-Control Data | 1.2-3 | 1.2-3 |
multiplex Algebraic Tools for the Analysis of Multiple Social Networks | 3.1.1 | 3.1.1 |
multipol Multivariate Polynomials | 1.0-9 | 1.0-9 |
MultiRNG Multivariate Pseudo-Random Number Generation | 1.2.4 | 1.2.4 |
multiROC Calculating and Visualizing ROC and PR Curves Across Multi-Class Classifications | 1.1.1 | 1.1.1 |
MultisiteMediation Causal Mediation Analysis in Multisite Trials | 0.0.4 | 0.0.4 |
multitaper Spectral Analysis Tools using the Multitaper Method | 1.0-17 | 1.0-17 |
multiway Component Models for Multi-Way Data | 1.0-6 | 1.0-6 |
multiwayvcov Multi-Way Standard Error Clustering | 1.2.3 | 1.2.3 |
MuMIn Multi-Model Inference | 1.47.5 | 1.47.5 |
munfold Metric Unfolding | 0.3.5 | 0.3.5 |
munsell Utilities for Using Munsell Colours | 0.5.0 | 0.5.0 |
musica Multiscale Climate Model Assessment | 0.1.3 | 0.1.3 |
mvglmmRank Multivariate Generalized Linear Mixed Models for Ranking Sports Teams | 1.2-4 | 1.2-4 |
mvGPS Causal Inference using Multivariate Generalized Propensity Score | 1.2.2 | 1.2.2 |
mvhtests Multivariate Hypothesis Tests | 1.0 | 1.0 |
mvLSW Multivariate, Locally Stationary Wavelet Process Estimation | 1.2.5 | 1.2.5 |
mvmesh Multivariate Meshes and Histograms in Arbitrary Dimensions | 1.6 | 1.6 |
mvmeta Multivariate and Univariate Meta-Analysis and Meta-Regression | 1.0.3 | 1.0.3 |
mvna Nelson-Aalen Estimator of the Cumulative Hazard in Multistate Models | 2.0.1 | 2.0.1 |
mvnfast Fast Multivariate Normal and Student's t Methods | 0.2.8 | 0.2.8 |
mvnormtest Normality Test for Multivariate Variables | 0.1-9 | 0.1-9 |
mvoutlier Multivariate Outlier Detection Based on Robust Methods | 2.1.1 | 2.1.1 |
mvp Fast Symbolic Multivariate Polynomials | 1.0-14 | 1.0-14 |
mvprpb Orthant Probability of the Multivariate Normal Distribution | 1.0.4 | 1.0.4 |
mvQuad Methods for Multivariate Quadrature | 1.0-8 | 1.0-8 |
mvrtn Mean and Variance of Truncated Normal Distribution | 1.0 | 1.0 |
mvtmeta Multivariate Meta-Analysis | 1.1 | 1.1 |
mvtnorm Multivariate Normal and t Distributions | 1.2-4 | 1.2-4 |
mvtsplot Multivariate Time Series Plot | 1.0-4 | 1.0-4 |
mxkssd Efficient Mixed-Level k-Circulant Supersaturated Designs | 1.2 | 1.2 |
n1qn1 Port of the 'Scilab' 'n1qn1' Module for Unconstrained BFGS Optimization | 6.0.1-11 | 6.0.1-11 |
nabor Wraps 'libnabo', a Fast K Nearest Neighbour Library for Low Dimensions | 0.5.0 | 0.5.0 |
NADA Nondetects and Data Analysis for Environmental Data | 1.6-1.1 | 1.6-1.1 |
NADIA NA Data Imputation Algorithms | 0.4.2 | 0.4.2 |
NAEPirtparams IRT Parameters for the National Assessment of Education Progress | 1.0.0 | 1.0.0 |
NAEPprimer The NAEP Primer | 1.0.1 | 1.0.1 |
naivebayes High Performance Implementation of the Naive Bayes Algorithm | 0.9.7 | 0.9.7 |
naniar Data Structures, Summaries, and Visualisations for Missing Data | 1.0.0 | 1.0.0 |
nanoarrow Interface to the 'nanoarrow' 'C' Library | 0.3.0.1 | 0.3.0.1 |
nanonext NNG (Nanomsg Next Gen) Lightweight Messaging Library | 0.12.0 | 0.12.0 |
nanotime Nanosecond-Resolution Time Support for R | 0.3.10 | 0.3.10 |
nardl Nonlinear Cointegrating Autoregressive Distributed Lag Model | 0.1.6 | 0.1.6 |
narray Subset- And Name-Aware Array Utility Functions | 0.5.1 | 0.5.1 |
nasapower NASA POWER API Client | 4.1.0 | 4.1.0 |
natural Estimating the Error Variance in a High-Dimensional Linear Model | 0.9.0 | 0.9.0 |
NBAloveR Help Basketball Data Analysis | 0.1.3.3 | 0.1.3.3 |
nbapalettes An NBA Jersey Palette Generator | 0.1.0 | 0.1.0 |
NbClust Determining the Best Number of Clusters in a Data Set | 3.0.1 | 3.0.1 |
nbTransmission Naive Bayes Transmission Analysis | 1.1.3 | 1.1.3 |
ncappc NCA Calculations and Population Model Diagnosis | 0.3.0 | 0.3.0 |
ncar Noncompartmental Analysis for Pharmacokinetic Report | 0.5.0 | 0.5.0 |
ncbit Retrieve and Build NBCI Taxonomic Data | 2013.03.29.1 | 2013.03.29.1 |
ncdf4 Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files | 1.22 | 1.22 |
ncdfgeom 'NetCDF' Geometry and Time Series | 1.1.5 | 1.1.5 |
nCDunnett Noncentral Dunnett's Test Distribution | 1.1.0 | 1.1.0 |
ncf Spatial Covariance Functions | 1.3-2 | 1.3-2 |
ncmeta Straightforward 'NetCDF' Metadata | 0.3.6 | 0.3.6 |
NCmisc Miscellaneous Functions for Creating Adaptive Functions and Scripts | 1.2.0 | 1.2.0 |
nCopula Hierarchical Archimedean Copulas Constructed with Multivariate Compound Distributions | 0.1.1 | 0.1.1 |
ncvreg Regularization Paths for SCAD and MCP Penalized Regression Models | 3.14.1 | 3.14.1 |
ndjson Wicked-Fast Streaming 'JSON' ('ndjson') Reader | 0.9.0 | 0.9.0 |
neldermead R Port of the 'Scilab' Neldermead Module | 1.0-12 | 1.0-12 |
netchain Inferring Causal Effects on Collective Outcomes under Interference | 0.2.0 | 0.2.0 |
NetLogoR Build and Run Spatially Explicit Agent-Based Models | 0.3.11 | 0.3.11 |
netmeta Network Meta-Analysis using Frequentist Methods | 2.9-0 | 2.9-0 |
netrankr Analyzing Partial Rankings in Networks | 1.1.1 | 1.1.1 |
nets Network Estimation for Time Series | 0.9.1 | 0.9.1 |
netSEM Network Structural Equation Modeling | 0.5.1 | 0.5.1 |
network Classes for Relational Data | 1.18.2 | 1.18.2 |
NetworkChange Bayesian Package for Network Changepoint Analysis | 0.8 | 0.8 |
NetworkComparisonTest Statistical Comparison of Two Networks Based on Several Invariance Measures | 2.2.2 | 2.2.2 |
networkD3 D3 JavaScript Network Graphs from R | 0.4 | 0.4 |
networkDynamic Dynamic Extensions for Network Objects | 0.11.4 | 0.11.4 |
networkLite An Simplified Implementation of the 'network' Package Functionality | 1.0.5 | 1.0.5 |
NetworkRiskMeasures Risk Measures for (Financial) Networks | 0.1.4 | 0.1.4 |
NetworkToolbox Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis | 1.4.2 | 1.4.2 |
networktools Tools for Identifying Important Nodes in Networks | 1.5.1 | 1.5.1 |
networktree Recursive Partitioning of Network Models | 1.0.1 | 1.0.1 |
neuralnet Training of Neural Networks | 1.44.2 | 1.44.2 |
NeuralNetTools Visualization and Analysis Tools for Neural Networks | 1.5.3 | 1.5.3 |
neuroim Data Structures and Handling for Neuroimaging Data | 0.0.6 | 0.0.6 |
neuRosim Simulate fMRI Data | 0.2-14 | 0.2-14 |
Newdistns Computes Pdf, Cdf, Quantile and Random Numbers, Measures of Inference for 19 General Families of Distributions | 2.1 | 2.1 |
nFactors Parallel Analysis and Other Non Graphical Solutions to the Cattell Scree Test | 2.4.1.1 | 2.4.1.1 |
NFCP N-Factor Commodity Pricing Through Term Structure Estimation | 1.2.1 | 1.2.1 |
nflfastR Functions to Efficiently Access NFL Play by Play Data | 4.6.1 | 4.6.1 |
nflplotR NFL Logo Plots in 'ggplot2' and 'gt' | 1.2.0 | 1.2.0 |
nflreadr Download 'nflverse' Data | 1.4.0 | 1.4.0 |
nflseedR Functions to Efficiently Simulate and Evaluate NFL Seasons | 1.2.0 | 1.2.0 |
NFLSimulatoR Simulating Plays and Drives in the NFL | 0.4.0 | 0.4.0 |
ngram Fast n-Gram 'Tokenization' | 3.2.3 | 3.2.3 |
ngramrr A Simple General Purpose N-Gram Tokenizer | 0.2.0 | 0.2.0 |
ngspatial Fitting the Centered Autologistic and Sparse Spatial Generalized Linear Mixed Models for Areal Data | 1.2-2 | 1.2-2 |
NGSSEML Non-Gaussian State-Space with Exact Marginal Likelihood | 2.2 | 2.2 |
nhanesA NHANES Data Retrieval | 1.0 | 1.0 |
nhdplusTools NHDPlus Tools | 1.0.0 | 1.0.0 |
nhdR Tools for Working with the National Hydrography Dataset | 0.6.1 | 0.6.1 |
nhlapi A Minimum-Dependency 'R' Interface to the 'NHL' API | 0.1.4 | 0.1.4 |
NHLData Scores for Every Season Since the Founding of the NHL in 1917 | 1.0.0 | 1.0.0 |
nhlscrape Scrapes the 'NHL' API for Statistical Analysis | 0.1.3 | 0.1.3 |
nifti.io Read and Write NIfTI Files | 1.0.0 | 1.0.0 |
nilde Nonnegative Integer Solutions of Linear Diophantine Equations with Applications | 1.1-7 | 1.1-7 |
nimble MCMC, Particle Filtering, and Programmable Hierarchical Modeling | 1.1.0 | 1.1.0 |
nipals Principal Components Analysis using NIPALS or Weighted EMPCA, with Gram-Schmidt Orthogonalization | 0.8 | 0.8 |
NIRStat Novel Statistical Methods for Studying Near-Infrared Spectroscopy (NIRS) Time Series Data | 1.1 | 1.1 |
NISTunits Fundamental Physical Constants and Unit Conversions from NIST | 1.0.1 | 1.0.1 |
NlcOptim Solve Nonlinear Optimization with Nonlinear Constraints | 0.6 | 0.6 |
nleqslv Solve Systems of Nonlinear Equations | 3.3.5 | 3.3.5 |
NlinTS Models for Non Linear Causality Detection in Time Series | 1.4.5 | 1.4.5 |
nlist Lists of Numeric Atomic Objects | 0.3.3 | 0.3.3 |
nlme Linear and Nonlinear Mixed Effects Models | 3.1-165 | 3.1-165 |
nlmixr Nonlinear Mixed Effects Models in Population PK/PD | 2.0.7 | 2.0.7 |
nlmrt Functions for Nonlinear Least Squares Solutions | 2016.3.2 | 2016.3.2 |
nloptr R Interface to NLopt | 2.0.3 | 2.0.3 |
NLP Natural Language Processing Infrastructure | 0.2-1 | 0.2-1 |
nlreg Higher Order Inference for Nonlinear Heteroscedastic Models | 1.2-2.2 | 1.2-2.2 |
nls2 Non-Linear Regression with Brute Force | 0.3-3 | 0.3-3 |
nlsem Fitting Structural Equation Mixture Models | 0.8-1 | 0.8-1 |
nlsr Functions for Nonlinear Least Squares Solutions - Updated 2022 | 2023.8.31 | 2023.8.31 |
nlstools Tools for Nonlinear Regression Analysis | 2.0-0 | 2.0-0 |
nlts Nonlinear Time Series Analysis | 1.0-2 | 1.0-2 |
nmadb Network Meta-Analysis Database API | 1.2.0 | 1.2.0 |
NMADiagT Network Meta-Analysis of Multiple Diagnostic Tests | 0.1.2 | 0.1.2 |
nmaINLA Network Meta-Analysis using Integrated Nested Laplace Approximations | 1.1.0 | 1.1.0 |
NMAoutlier Detecting Outliers in Network Meta-Analysis | 0.1.18 | 0.1.18 |
nmaplateplot The Plate Plot for Network Meta-Analysis Results | 1.0.1 | 1.0.1 |
nmarank Complex Hierarchy Questions in Network Meta-Analysis | 0.2-3 | 0.2-3 |
nmathresh Thresholds and Invariant Intervals for Network Meta-Analysis | 0.1.6 | 0.1.6 |
NMF Algorithms and Framework for Nonnegative Matrix Factorization (NMF) | 0.28 | 0.28 |
NMOF Numerical Methods and Optimization in Finance | 2.10-0 | 2.10-0 |
nmw Understanding Nonlinear Mixed Effects Modeling for Population Pharmacokinetics | 0.1.5 | 0.1.5 |
nnet Feed-Forward Neural Networks and Multinomial Log-Linear Models | 7.3-19 | 7.3-19 |
nnfor Time Series Forecasting with Neural Networks | 0.9.9 | 0.9.9 |
nnls The Lawson-Hanson Algorithm for Non-Negative Least Squares (NNLS) | 1.5 | 1.5 |
NNS Nonlinear Nonparametric Statistics | 10.5 | 10.5 |
nnTensor Non-Negative Tensor Decomposition | 1.1.8 | 1.1.8 |
nodeHarvest Node Harvest for Regression and Classification | 0.7-3 | 0.7-3 |
nomisr Access 'Nomis' UK Labour Market Data | 0.4.7 | 0.4.7 |
nomnoml Sassy 'UML' Diagrams | 0.2.7 | 0.2.7 |
NonCompart Noncompartmental Analysis for Pharmacokinetic Data | 0.7.0 | 0.7.0 |
nonlinearICP Invariant Causal Prediction for Nonlinear Models | 0.1.2.1 | 0.1.2.1 |
nonlinearTseries Nonlinear Time Series Analysis | 0.2.12 | 0.2.12 |
nonneg.cg Non-Negative Conjugate-Gradient Minimizer | 0.1.6-1 | 0.1.6-1 |
nonnest2 Tests of Non-Nested Models | 0.5-6 | 0.5-6 |
NonNorMvtDist Multivariate Lomax (Pareto Type II) and Its Related Distributions | 1.0.2 | 1.0.2 |
nor1mix Normal aka Gaussian 1-d Mixture Models | 1.3-2 | 1.3-2 |
norm Analysis of Multivariate Normal Datasets with Missing Values | 1.0-11.0 | 1.0-11.0 |
norm2 Analysis of Incomplete Multivariate Data under a Normal Model | 2.0.4 | 2.0.4 |
NormalGamma Normal-gamma convolution model | 1.1 | 1.1 |
NormalLaplace The Normal Laplace Distribution | 0.3-1 | 0.3-1 |
normalp Routines for Exponential Power Distribution | 0.7.2.1 | 0.7.2.1 |
nortest Tests for Normality | 1.0-4 | 1.0-4 |
nosoi A Forward Agent-Based Transmission Chain Simulator | 1.1.0 | 1.1.0 |
notifyme Send Alerts to your Cellphone and Phillips Hue Lights | 0.3.0 | 0.3.0 |
Nozzle.R1 Nozzle Reports | 1.1-1.1 | 1.1-1.1 |
np Nonparametric Kernel Smoothing Methods for Mixed Data Types | 0.60-17 | 0.60-17 |
nparACT Non-Parametric Measures of Actigraphy Data | 0.8 | 0.8 |
NPBayesImputeCat Non-Parametric Bayesian Multiple Imputation for Categorical Data | 0.5 | 0.5 |
npde Normalised Prediction Distribution Errors for Nonlinear Mixed-Effect Models | 3.4 | 3.4 |
NPHMC Sample Size Calculation for the Proportional Hazards Mixture Cure Model | 2.3 | 2.3 |
NPMLEcmprsk Type-Specific Failure Rate and Hazard Rate on Competing Risks Data | 3.0 | 3.0 |
nppbib Nonparametric Partially-Balanced Incomplete Block Design Analysis | 1.2-0 | 1.2-0 |
NPRED Predictor Identifier: Nonparametric Prediction | 1.0.7 | 1.0.7 |
npst Generalization of Hewitt's Seasonality Test | 2.0 | 2.0 |
npsurv Nonparametric Survival Analysis | 0.5-0 | 0.5-0 |
nsarfima Methods for Fitting and Simulating Non-Stationary ARFIMA Models | 0.2.0.0 | 0.2.0.0 |
nse Numerical Standard Errors Computation in R | 1.21 | 1.21 |
nsga2R Elitist Non-Dominated Sorting Genetic Algorithm | 1.1 | 1.1 |
NSM3 Functions and Datasets to Accompany Hollander, Wolfe, and Chicken - Nonparametric Statistical Methods, Third Edition | 1.16 | 1.16 |
nsprcomp Non-Negative and Sparse PCA | 0.5.1-2 | 0.5.1-2 |
nsRFA Non-Supervised Regional Frequency Analysis | 0.7-16 | 0.7-16 |
NTS Nonlinear Time Series Analysis | 1.1.3 | 1.1.3 |
nullabor Tools for Graphical Inference | 0.3.12 | 0.3.12 |
numbers Number-Theoretic Functions | 0.8-5 | 0.8-5 |
numDeriv Accurate Numerical Derivatives | 2016.8-1.1 | 2016.8-1.1 |
nvmix Multivariate Normal Variance Mixtures | 0.1-0 | 0.1-0 |
nycflights13 Flights that Departed NYC in 2013 | 1.0.2 | 1.0.2 |
o2geosocial Reconstruction of Transmission Chains from Surveillance Data | 1.1.1 | 1.1.1 |
oai General Purpose 'Oai-PMH' Services Client | 0.4.0 | 0.4.0 |
OAIHarvester Harvest Metadata Using OAI-PMH Version 2.0 | 0.3-4 | 0.3-4 |
Oarray Arrays with Arbitrary Offsets | 1.4-9 | 1.4-9 |
OasisR Outright Tool for the Analysis of Spatial Inequalities and Segregation | 3.1.0 | 3.1.0 |
obAnalytics Limit Order Book Analytics | 0.1.1 | 0.1.1 |
OBsMD Objective Bayesian Model Discrimination in Follow-Up Designs | 11.1 | 11.1 |
occ Estimation of PET Neuroreceptor Occupancies | 1.1 | 1.1 |
oce Analysis of Oceanographic Data | 1.8-2 | 1.8-2 |
od Manipulate and Map Origin-Destination Data | 0.4.0 | 0.4.0 |
odbc Connect to ODBC Compatible Databases (using the DBI Interface) | 1.5.0 | 1.5.0 |
odds.converter Betting Odds Conversion | 1.4.8 | 1.4.8 |
oddsapiR Access Live Sports Odds from the Odds API | 0.0.3 | 0.0.3 |
odeintr C++ ODE Solvers Compiled on-Demand | 1.7.1 | 1.7.1 |
odin ODE Generation and Integration | 1.2.4 | 1.2.4 |
odpc One-Sided Dynamic Principal Components | 2.0.5 | 2.0.5 |
odr Optimal Design and Statistical Power for Experimental Studies Investigating Main, Mediation, and Moderation Effects | 1.4.4 | 1.4.4 |
OECD Search and Extract Data from the OECD | 0.2.5 | 0.2.5 |
officer Manipulation of Microsoft Word and PowerPoint Documents | 0.6.3 | 0.6.3 |
ofGEM A Meta-Analysis Approach with Filtering for Identifying Gene-Level Gene-Environment Interactions with Genetic Association Data | 1.0 | 1.0 |
ohoegdm Ordinal Higher-Order Exploratory General Diagnostic Model for Polytomous Data | 0.1.0 | 0.1.0 |
oligo | 1.62.1 | 1.62.1 |
oligoClasses | 1.60.0 | 1.60.0 |
olsrr Tools for Building OLS Regression Models | 0.5.3 | 0.5.3 |
ompr Model and Solve Mixed Integer Linear Programs | 1.0.4 | 1.0.4 |
onbrand Templated Reporting Workflows in Word and PowerPoint | 1.0.4 | 1.0.4 |
OneR One Rule Machine Learning Classification Algorithm with Enhancements | 2.2 | 2.2 |
OneStep One-Step Estimation | 0.9.2 | 0.9.2 |
onion Octonions and Quaternions | 1.5-0 | 1.5-0 |
onlineforecast Forecast Modelling for Online Applications | 1.0.2 | 1.0.2 |
onnx R Interface to 'ONNX' | 0.0.3 | 0.0.3 |
ooplah Helper Functions for Class Object-Oriented Programming | 0.2.0 | 0.2.0 |
OOR Optimistic Optimization in R | 0.1.4 | 0.1.4 |
opdisDownsampling Optimal Distribution Preserving Down-Sampling of Bio-Medical Data | 0.8.3 | 0.8.3 |
OPDOE Optimal Design of Experiments | 1.0-10 | 1.0-10 |
openadds Client to Access 'Openaddresses' Data | 0.2.0 | 0.2.0 |
openair Tools for the Analysis of Air Pollution Data | 2.18-0 | 2.18-0 |
openalexR Getting Bibliographic Records from 'OpenAlex' Database Using 'DSL' API | 1.4.0 | 1.4.0 |
opencage Geocode with the OpenCage API | 0.2.2 | 0.2.2 |
opencpu Producing and Reproducing Results | 2.2.11 | 2.2.11 |
opendotaR Interface for OpenDota API | 0.1.4 | 0.1.4 |
openEBGM EBGM Disproportionality Scores for Adverse Event Data Mining | 0.9.1 | 0.9.1 |
openintro Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs | 2.4.0 | 2.4.0 |
OpenML Open Machine Learning and Open Data Platform | 1.12 | 1.12 |
OpenMx Extended Structural Equation Modelling | 2.21.13 | 2.21.13 |
openNLP Apache OpenNLP Tools Interface | 0.2-7 | 0.2-7 |
openNLPdata Apache OpenNLP Jars and Basic English Language Models | 1.5.3-4 | 1.5.3-4 |
opensensmapr Client for the Data API of 'openSenseMap.org' | 0.6.0 | 0.6.0 |
openssl Toolkit for Encryption, Signatures and Certificates Based on OpenSSL | 2.0.6 | 2.0.6 |
openSTARS An Open Source Implementation of the 'ArcGIS' Toolbox 'STARS' | 1.2.3 | 1.2.3 |
OpenStreetMap Access to Open Street Map Raster Images | 0.4.0 | 0.4.0 |
opentraj Tools for Creating and Analysing Air Trajectory Data | 1.0 | 1.0 |
opentripplanner Setup and connect to 'OpenTripPlanner' | 0.4.0 | 0.4.0 |
openxlsx Read, Write and Edit xlsx Files | 4.2.5.2 | 4.2.5.2 |
opera Online Prediction by Expert Aggregation | 1.2.0 | 1.2.0 |
operator.tools Utilities for Working with R's Operators | 1.6.3 | 1.6.3 |
operators Additional Binary Operators | 0.1-8 | 0.1-8 |
optbdmaeAT Optimal Block Designs for Two-Colour cDNA Microarray Experiments | 1.0.1 | 1.0.1 |
optextras Tools to Support Optimization Possibly with Bounds and Masks | 2019-12.4 | 2019-12.4 |
OptGS Near-Optimal Group-Sequential Designs for Continuous Outcomes | 1.1.1 | 1.1.1 |
OptHedging Estimation of value and hedging strategy of call and put options. | 1.0 | 1.0 |
OptimalCutpoints Computing Optimal Cutpoints in Diagnostic Tests | 1.1-5 | 1.1-5 |
OptimalDesign A Toolbox for Computing Efficient Designs of Experiments | 1.0.1 | 1.0.1 |
OptimaRegion Confidence Regions for Optima of Response Surfaces | 1.2 | 1.2 |
optimbase R Port of the 'Scilab' Optimbase Module | 1.0-10 | 1.0-10 |
optimParallel Parallel Version of the L-BFGS-B Optimization Method | 1.0-2 | 1.0-2 |
optimr A Replacement and Extension of the 'optim' Function | 2019-12.16 | 2019-12.16 |
optimsimplex R Port of the 'Scilab' Optimsimplex Module | 1.0-8 | 1.0-8 |
optimx Expanded Replacement and Extension of the 'optim' Function | 2023-10.21 | 2023-10.21 |
OptionPricing Option Pricing with Efficient Simulation Algorithms | 0.1.2 | 0.1.2 |
optiscale Optimal Scaling | 1.2.2 | 1.2.2 |
optiSolve Linear, Quadratic, and Rational Optimization | 1.0 | 1.0 |
optmatch Functions for Optimal Matching | 0.10.7 | 0.10.7 |
optparse Command Line Option Parser | 1.7.3 | 1.7.3 |
optR Optimization Toolbox for Solving Linear Systems | 1.2.5 | 1.2.5 |
optrcdmaeAT Optimal Row-Column Designs for Two-Colour cDNA Microarray Experiments | 1.0.0 | 1.0.0 |
optweight Targeted Stable Balancing Weights Using Optimization | 0.2.5 | 0.2.5 |
opusminer OPUS Miner Algorithm for Filtered Top-k Association Discovery | 0.1-1 | 0.1-1 |
OpVaR Statistical Methods for Modelling Operational Risk | 1.2 | 1.2 |
ORDER2PARENT Estimate parent distributions with data of several order statistics | 1.0 | 1.0 |
orderly Lightweight Reproducible Reporting | 1.4.3 | 1.4.3 |
OrdFacReg Least Squares, Logistic, and Cox-Regression with Ordered Predictors | 1.0.6 | 1.0.6 |
ordinal Regression Models for Ordinal Data | 2023.12-4 | 2023.12-4 |
OrdNor Concurrent Generation of Ordinal and Normal Data with Given Correlation Matrix and Marginal Distributions | 2.2.3 | 2.2.3 |
ore An R Interface to the Onigmo Regular Expression Library | 1.7.4.1 | 1.7.4.1 |
org.At.tair.db | 3.16.0 | 3.16.0 |
org.Hs.eg.db | 3.16.0 | 3.16.0 |
org.Mm.eg.db | 3.19.1 | 3.19.1 |
OrgMassSpecR Organic Mass Spectrometry | 0.5-3 | 0.5-3 |
ORIClust Order-Restricted Information Criterion-Based Clustering Algorithm | 1.0-2 | 1.0-2 |
orloca Operations Research LOCational Analysis Models | 5.3 | 5.3 |
oro.dicom Rigorous - DICOM Input / Output | 0.5.3 | 0.5.3 |
oro.nifti Rigorous - 'NIfTI' + 'ANALYZE' + 'AFNI' : Input / Output | 0.11.4 | 0.11.4 |
oro.pet Rigorous - Positron Emission Tomography | 0.2.7 | 0.2.7 |
orthogonalsplinebasis Orthogonal B-Spline Basis Functions | 0.1.7 | 0.1.7 |
OrthoPanels Dynamic Panel Models with Orthogonal Reparameterization of Fixed Effects | 1.2-4 | 1.2-4 |
orthopolynom Collection of Functions for Orthogonal and Orthonormal Polynomials | 1.0-5 | 1.0-5 |
osDesign Design, Planning and Analysis of Observational Studies | 1.8 | 1.8 |
osmar OpenStreetMap and R | 1.1-7 | 1.1-7 |
osmdata Import 'OpenStreetMap' Data as Simple Features or Spatial Objects | 0.2.5 | 0.2.5 |
osmextract Download and Import Open Street Map Data Extracts | 0.5.0 | 0.5.0 |
osqp Quadratic Programming Solver using the 'OSQP' Library | 0.6.3.2 | 0.6.3.2 |
osrm Interface Between R and the OpenStreetMap-Based Routing Service OSRM | 4.1.1 | 4.1.1 |
otrimle Robust Model-Based Clustering | 2.0 | 2.0 |
OTRselect Variable Selection for Optimal Treatment Decision | 1.2 | 1.2 |
otsad Online Time Series Anomaly Detectors | 0.2.0 | 0.2.0 |
ouch Ornstein-Uhlenbeck Models for Phylogenetic Comparative Hypotheses | 2.19 | 2.19 |
outbreaker2 Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data | 1.1.3 | 1.1.3 |
outbreaks A Collection of Disease Outbreak Data | 1.9.0 | 1.9.0 |
OutlierDM Outlier Detection for Multi-replicated High-throughput Data | 1.1.1 | 1.1.1 |
outliers Tests for Outliers | 0.15 | 0.15 |
overlapping Estimation of Overlapping in Empirical Distributions | 2.0 | 2.0 |
OVtool Omitted Variable Tool | 1.0.3 | 1.0.3 |
OwenQ Owen Q-Function | 1.0.7 | 1.0.7 |
ows4R Interface to OGC Web-Services (OWS) | 0.3-6 | 0.3-6 |
pa Performance Attribution for Equity Portfolios | 1.2-4 | 1.2-4 |
pacbpred PAC-Bayesian Estimation and Prediction in Sparse Additive Models. | 0.92.2 | 0.92.2 |
packcircles Circle Packing | 0.3.6 | 0.3.6 |
packrat A Dependency Management System for Projects and their R Package Dependencies | 0.9.2 | 0.9.2 |
pacman Package Management Tool | 0.5.1 | 0.5.1 |
Pade Padé Approximant Coefficients | 1.0.6 | 1.0.6 |
padr Quickly Get Datetime Data Ready for Analysis | 0.6.2 | 0.6.2 |
paf Attributable Fraction Function for Censored Survival Data | 1.0 | 1.0 |
pagedown Paginate the HTML Output of R Markdown with CSS for Print | 0.20 | 0.20 |
pageviews An API Client for Wikimedia Traffic Data | 0.5.0 | 0.5.0 |
PairedData Paired Data Analysis | 1.1.1 | 1.1.1 |
pairwise Rasch Model Parameters by Pairwise Algorithm | 0.6.1-0 | 0.6.1-0 |
pak Another Approach to Package Installation | 0.8.0 | 0.8.0 |
palasso Sparse Regression with Paired Covariates | 0.0.8 | 0.0.8 |
paleoTS Analyze Paleontological Time-Series | 0.5.3 | 0.5.3 |
palmerpenguins Palmer Archipelago (Antarctica) Penguin Data | 0.1.1 | 0.1.1 |
palmtree Partially Additive (Generalized) Linear Model Trees | 0.9-1 | 0.9-1 |
pammtools Piece-Wise Exponential Additive Mixed Modeling Tools for Survival Analysis | 0.5.92 | 0.5.92 |
pampe Implementation of the Panel Data Approach Method for Program Evaluation | 1.1.2 | 1.1.2 |
pamr Pam: Prediction Analysis for Microarrays | 1.56.1 | 1.56.1 |
pan Multiple Imputation for Multivariate Panel or Clustered Data | 1.9 | 1.9 |
pander An R 'Pandoc' Writer | 0.6.5 | 0.6.5 |
panelaggregation Aggregate Longitudinal Survey Data | 0.1.1 | 0.1.1 |
Paneldata Linear models for panel data | 1.0 | 1.0 |
PanelMatch Matching Methods for Causal Inference with Time-Series Cross-Sectional Data | 2.0.1 | 2.0.1 |
panelvar Panel Vector Autoregression | 0.5.5 | 0.5.5 |
papaja Prepare American Psychological Association Journal Articles with R Markdown | 0.1.3 | 0.1.3 |
papeR A Toolbox for Writing Pretty Papers and Reports | 1.0-5 | 1.0-5 |
paradox Define and Work with Parameter Spaces for Complex Algorithms | 0.11.1 | 0.11.1 |
parallel | 4.4.1 | 4.4.1 |
parallelly Enhancing the 'parallel' Package | 1.36.0 | 1.36.0 |
parallelMap Unified Interface to Parallelization Back-Ends | 1.5.1 | 1.5.1 |
param6 A Fast and Lightweight R6 Parameter Interface | 0.2.4 | 0.2.4 |
parameters Processing of Model Parameters | 0.21.3 | 0.21.3 |
ParamHelpers Helpers for Parameters in Black-Box Optimization, Tuning and Machine Learning | 1.14.1 | 1.14.1 |
params Simplify Parameters | 0.7.3 | 0.7.3 |
paran Horn's Test of Principal Components/Factors | 1.5.2 | 1.5.2 |
ParetoPosStable Computing, Fitting and Validating the PPS Distribution | 1.1 | 1.1 |
parfm Parametric Frailty Models | 2.7.6 | 2.7.6 |
parma Portfolio Allocation and Risk Management Applications | 1.7 | 1.7 |
parmigene Parallel Mutual Information Estimation for Gene Network Reconstruction | 1.1.0 | 1.1.0 |
parsedate Recognize and Parse Dates in Various Formats, Including All ISO 8601 Formats | 1.3.1 | 1.3.1 |
parsetools Parse Tools | 0.1.3 | 0.1.3 |
parSim Parallel Simulation Studies | 0.1.5 | 0.1.5 |
parsnip A Common API to Modeling and Analysis Functions | 1.1.1 | 1.1.1 |
partitions Additive Partitions of Integers | 1.10-4 | 1.10-4 |
partsm Periodic Autoregressive Time Series Models | 1.1-3 | 1.1-3 |
party A Laboratory for Recursive Partytioning | 1.3-13 | 1.3-13 |
partykit A Toolkit for Recursive Partytioning | 1.2-20 | 1.2-20 |
pastecs Package for Analysis of Space-Time Ecological Series | 1.3.21 | 1.3.21 |
patchDVI Package to Patch '.dvi' or '.synctex' Files | 1.10.1 | 1.10.1 |
patchSynctex Communication Between Editor and Viewer for Literate Programs | 0.1-4 | 0.1-4 |
patchwork The Composer of Plots | 1.2.0 | 1.2.0 |
paths An Imputation Approach to Estimating Path-Specific Causal Effects | 0.1.1 | 0.1.1 |
patrick Parameterized Unit Testing | 0.1.0 | 0.1.0 |
pawacc Physical Activity with Accelerometers | 1.2.2 | 1.2.2 |
paws Amazon Web Services Software Development Kit | 0.5.0 | 0.5.0 |
paws.analytics 'Amazon Web Services' Analytics Services | 0.5.0 | 0.5.0 |
paws.application.integration 'Amazon Web Services' Application Integration Services | 0.5.0 | 0.5.0 |
paws.common Paws Low-Level Amazon Web Services API | 0.7.0 | 0.7.0 |
paws.compute 'Amazon Web Services' Compute Services | 0.5.0 | 0.5.0 |
paws.cost.management 'Amazon Web Services' Cost Management Services | 0.5.0 | 0.5.0 |
paws.customer.engagement 'Amazon Web Services' Customer Engagement Services | 0.5.0 | 0.5.0 |
paws.database 'Amazon Web Services' Database Services | 0.5.0 | 0.5.0 |
paws.developer.tools 'Amazon Web Services' Developer Tools Services | 0.5.0 | 0.5.0 |
paws.end.user.computing 'Amazon Web Services' End User Computing Services | 0.5.0 | 0.5.0 |
paws.machine.learning 'Amazon Web Services' Machine Learning Services | 0.5.0 | 0.5.0 |
paws.management 'Amazon Web Services' Management & Governance Services | 0.5.0 | 0.5.0 |
paws.networking 'Amazon Web Services' Networking & Content Delivery Services | 0.5.0 | 0.5.0 |
paws.security.identity 'Amazon Web Services' Security, Identity, & Compliance Services | 0.5.0 | 0.5.0 |
paws.storage 'Amazon Web Services' Storage Services | 0.5.0 | 0.5.0 |
pbapply Adding Progress Bar to '*apply' Functions | 1.7-2 | 1.7-2 |
pbdMPI R Interface to MPI for HPC Clusters (Programming with Big Data Project) | 0.5-1 | 0.5-1 |
pbdZMQ Programming with Big Data -- Interface to 'ZeroMQ' | 0.3-13 | 0.3-13 |
PBIBD Partially Balanced Incomplete Block Designs | 1.3 | 1.3 |
pbivnorm Vectorized Bivariate Normal CDF | 0.6.0 | 0.6.0 |
pbkrtest Parametric Bootstrap, Kenward-Roger and Satterthwaite Based Methods for Test in Mixed Models | 0.5.2 | 0.5.2 |
pbmcapply Tracking the Progress of Mc*pply with Progress Bar | 1.5.1 | 1.5.1 |
pbo Probability of Backtest Overfitting | 1.3.4 | 1.3.4 |
pbs Periodic B Splines | 1.1 | 1.1 |
PBSddesolve Solver for Delay Differential Equations | 1.13.4 | 1.13.4 |
PBSmapping Mapping Fisheries Data and Spatial Analysis Tools | 2.73.4 | 2.73.4 |
PBSmodelling GUI Tools Made Easy: Interact with Models and Explore Data | 2.69.3 | 2.69.3 |
pbv Probabilities for Bivariate Normal Distribution | 0.5-47 | 0.5-47 |
PCA4TS Segmenting Multiple Time Series by Contemporaneous Linear Transformation | 0.1 | 0.1 |
pcaMethods | 1.96.0 | 1.96.0 |
pcaPP Robust PCA by Projection Pursuit | 2.0-4 | 2.0-4 |
PCAtools | 2.16.0 | 2.16.0 |
pcdpca Dynamic Principal Components for Periodically Correlated Functional Time Series | 0.4 | 0.4 |
pcFactorStan Stan Models for the Paired Comparison Factor Model | 1.5.4 | 1.5.4 |
pch Piecewise Constant Hazard Models for Censored and Truncated Data | 2.0 | 2.0 |
pchc Bayesian Network Learning with the PCHC and Related Algorithms | 1.2 | 1.2 |
PCICt Implementation of POSIXct Work-Alike for 365 and 360 Day Calendars | 0.5-4.4 | 0.5-4.4 |
pcIRT IRT Models for Polytomous and Continuous Item Responses | 0.2.4 | 0.2.4 |
PCMRS Model Response Styles in Partial Credit Models | 0.1-4 | 0.1-4 |
pcnetmeta Patient-Centered Network Meta-Analysis | 2.8 | 2.8 |
pco Panel Cointegration Tests | 1.0.1 | 1.0.1 |
pcse Panel-Corrected Standard Error Estimation in R | 1.9.1.1 | 1.9.1.1 |
pct Propensity to Cycle Tool | 0.9.3 | 0.9.3 |
pd.hg.u133.plus.2 | 3.12.0 | 3.12.0 |
pd.hg18.60mer.expr | 3.12.0 | 3.12.0 |
pd.hugene.2.0.st | 3.14.1 | 3.14.1 |
pd.mogene.2.1.st | 3.14.1 | 3.14.1 |
pd.mouse430.2 | 3.12.0 | 3.12.0 |
pd.mta.1.0 | 3.12.0 | 3.12.0 |
pd.porcine | 3.12.0 | 3.12.0 |
pdc Permutation Distribution Clustering | 1.0.3 | 1.0.3 |
pder Panel Data Econometrics with R | 1.0-2 | 1.0-2 |
pdfCluster Cluster Analysis via Nonparametric Density Estimation | 1.0-4 | 1.0-4 |
PDFEstimator Multivariate Nonparametric Probability Density Estimator | 4.3 | 4.3 |
pdfetch Fetch Economic and Financial Time Series Data from Public Sources | 0.2.9 | 0.2.9 |
pdftables Programmatic Conversion of PDF Tables | 0.1 | 0.1 |
pdftools Text Extraction, Rendering and Converting of PDF Documents | 3.4.0 | 3.4.0 |
pdist Partitioned Distance Function | 1.2.1 | 1.2.1 |
pdp Partial Dependence Plots | 0.8.1 | 0.8.1 |
PDQutils PDQ Functions via Gram Charlier, Edgeworth, and Cornish Fisher Approximations | 0.1.6 | 0.1.6 |
pdR Threshold Model and Unit Root Tests in Cross-Section and Time Series Data | 1.9.1 | 1.9.1 |
pdynmc Moment Condition Based Estimation of Linear Dynamic Panel Data Models | 0.9.10 | 0.9.10 |
PearsonDS Pearson Distribution System | 1.3.0 | 1.3.0 |
pec Prediction Error Curves for Risk Prediction Models in Survival Analysis | 2023.04.12 | 2023.04.12 |
PeerPerformance Luck-Corrected Peer Performance Analysis in R | 2.2.5 | 2.2.5 |
pegas Population and Evolutionary Genetics Analysis System | 1.3 | 1.3 |
pema Penalized Meta-Analysis | 0.1.3 | 0.1.3 |
penalized L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation in GLMs and in the Cox Model | 0.9-52 | 0.9-52 |
penalizedLDA Penalized Classification using Fisher's Linear Discriminant | 1.1 | 1.1 |
PenCoxFrail Regularization in Cox Frailty Models | 1.0.1 | 1.0.1 |
pendensity Density Estimation with a Penalized Mixture Approach | 0.2.13 | 0.2.13 |
penMSM Estimating Regularized Multi-state Models Using L1 Penalties | 0.99 | 0.99 |
peperr Parallelised Estimation of Prediction Error | 1.5 | 1.5 |
performance Assessment of Regression Models Performance | 0.10.8 | 0.10.8 |
PerformanceAnalytics Econometric Tools for Performance and Risk Analysis | 2.0.4 | 2.0.4 |
PermAlgo Permutational Algorithm to Simulate Survival Data | 1.2 | 1.2 |
permutations The Symmetric Group: Permutations of a Finite Set | 1.1-2 | 1.1-2 |
permute Functions for Generating Restricted Permutations of Data | 0.9-7 | 0.9-7 |
perry Resampling-Based Prediction Error Estimation for Regression Models | 0.3.1 | 0.3.1 |
pgirmess Spatial Analysis and Data Mining for Field Ecologists | 2.0.2 | 2.0.2 |
pglm Panel Generalized Linear Models | 0.2-3 | 0.2-3 |
PGM2 Nested Resolvable Designs and their Associated Uniform Designs | 1.0-1 | 1.0-1 |
pgmm Parsimonious Gaussian Mixture Models | 1.2.6 | 1.2.6 |
ph2bayes Bayesian Single-Arm Phase II Designs | 0.0.2 | 0.0.2 |
ph2bye Phase II Clinical Trial Design Using Bayesian Methods | 0.1.4 | 0.1.4 |
phangorn Phylogenetic Reconstruction and Analysis | 2.11.1 | 2.11.1 |
pharmaRTF Enhanced RTF Wrapper for Use with Existing Table Packages | 0.1.4 | 0.1.4 |
pheatmap Pretty Heatmaps | 1.0.12 | 1.0.12 |
PHEindicatormethods Common Public Health Statistics and their Confidence Intervals | 1.3.2 | 1.3.2 |
PHeval Evaluation of the Proportional Hazards Assumption and Test for Comparing Survival Curves with a Standardized Score Process | 0.5.4 | 0.5.4 |
philentropy Similarity and Distance Quantification Between Probability Functions | 0.8.0 | 0.8.0 |
phonics Phonetic Spelling Algorithms | 1.3.10 | 1.3.10 |
photobiology Photobiological Calculations | 0.10.11 | 0.10.11 |
photobiologyFilters Spectral Transmittance and Spectral Reflectance Data | 0.5.2 | 0.5.2 |
photobiologyWavebands Waveband Definitions for UV, VIS, and IR Radiation | 0.4.5 | 0.4.5 |
phyclust Phylogenetic Clustering (Phyloclustering) | 0.1-34 | 0.1-34 |
phylin Spatial Interpolation of Genetic Data | 2.0.2 | 2.0.2 |
phylobase Base Package for Phylogenetic Structures and Comparative Data | 0.8.12 | 0.8.12 |
phylolm Phylogenetic Linear Regression | 2.6.2 | 2.6.2 |
PhysicalActivity Process Accelerometer Data for Physical Activity Measurement | 0.2-4 | 0.2-4 |
phytools Phylogenetic Tools for Comparative Biology (and Other Things) | 2.1-1 | 2.1-1 |
picasso Pathwise Calibrated Sparse Shooting Algorithm | 1.3.1 | 1.3.1 |
pid Process Improvement using Data | 0.50 | 0.50 |
piecewiseSEM Piecewise Structural Equation Modeling | 2.3.0 | 2.3.0 |
pillar Coloured Formatting for Columns | 1.9.0 | 1.9.0 |
pimeta Prediction Intervals for Random-Effects Meta-Analysis | 1.1.3 | 1.1.3 |
pingr Check if a Remote Computer is Up | 2.0.3 | 2.0.3 |
pinnacle.data Market Odds Data from Pinnacle | 0.1.4 | 0.1.4 |
pipe.design Dual-Agent Dose Escalation for Phase I Trials using the PIPE Design | 0.5.1 | 0.5.1 |
pipeR Multi-Paradigm Pipeline Implementation | 0.6.1.3 | 0.6.1.3 |
PIPS Predicted Interval Plots | 1.0.1 | 1.0.1 |
piratings Calculate Pi Ratings for Teams Competing in Sport Matches | 0.1.9 | 0.1.9 |
pixmap Bitmap Images / Pixel Maps | 0.4-12 | 0.4-12 |
PK Basic Non-Compartmental Pharmacokinetics | 1.3-6 | 1.3-6 |
PKconverter The Parameter Converter of the Pharmacokinetic Models | 1.5 | 1.5 |
pkdata Creates Pharmacokinetic/Pharmacodynamic (PK/PD) Data | 0.1.0 | 0.1.0 |
pkgbuild Find Tools Needed to Build R Packages | 1.4.3 | 1.4.3 |
pkgcache Cache 'CRAN'-Like Metadata and R Packages | 2.2.1 | 2.2.1 |
pkgcond Classed Error and Warning Conditions | 0.1.1 | 0.1.1 |
pkgconfig Private Configuration for 'R' Packages | 2.0.3 | 2.0.3 |
pkgdepends Package Dependency Resolution and Downloads | 0.7.1 | 0.7.1 |
pkgdown Make Static HTML Documentation for a Package | 2.0.6 | 2.0.6 |
pkgfilecache Download and Manage Optional Package Data | 0.1.4 | 0.1.4 |
pkgKitten Create Simple Packages Which Do not Upset R Package Checks | 0.2.2 | 0.2.2 |
pkgload Simulate Package Installation and Attach | 1.3.4 | 1.3.4 |
pkgsearch Search and Query CRAN R Packages | 3.1.3 | 3.1.3 |
PKI Public Key Infrastucture for R Based on the X.509 Standard | 0.1-14 | 0.1-14 |
PKNCA Perform Pharmacokinetic Non-Compartmental Analysis | 0.10.2 | 0.10.2 |
PKPDmodels Pharmacokinetic/pharmacodynamic models | 0.3.2 | 0.3.2 |
pkr Pharmacokinetics in R | 0.1.3 | 0.1.3 |
PKreport A reporting pipeline for checking population pharmacokinetic model assumption | 1.5 | 1.5 |
pks Probabilistic Knowledge Structures | 0.6-0 | 0.6-0 |
plac A Pairwise Likelihood Augmented Cox Estimator for Left-Truncated Data | 0.1.3 | 0.1.3 |
PlackettLuce Plackett-Luce Models for Rankings | 0.4.3 | 0.4.3 |
plainview Plot Raster Images Interactively on a Plain HTML Canvas | 0.2.0 | 0.2.0 |
planar Multilayer Optics | 1.6 | 1.6 |
PlayerRatings Dynamic Updating Methods for Player Ratings Estimation | 1.1-0 | 1.1-0 |
plgp Particle Learning of Gaussian Processes | 1.1-12 | 1.1-12 |
plm Linear Models for Panel Data | 2.6-3 | 2.6-3 |
PLMIX Bayesian Analysis of Finite Mixtures of Plackett-Luce Models for Partial Rankings/Orderings | 2.1.1 | 2.1.1 |
PLmixed Estimate (Generalized) Linear Mixed Models with Factor Structures | 0.1.7 | 0.1.7 |
plogr The 'plog' C++ Logging Library | 0.2.0 | 0.2.0 |
plot3D Plotting Multi-Dimensional Data | 1.4 | 1.4 |
plot3Drgl Plotting Multi-Dimensional Data - Using 'rgl' | 1.0.4 | 1.0.4 |
plotdap Easily Visualize Data from 'ERDDAP' Servers via the 'rerddap' Package | 1.0.3 | 1.0.3 |
plotKML Visualization of Spatial and Spatio-Temporal Objects in Google Earth | 0.8-3 | 0.8-3 |
plotly Create Interactive Web Graphics via 'plotly.js' | 4.10.3 | 4.10.3 |
plotMCMC MCMC Diagnostic Plots | 2.0.1 | 2.0.1 |
plotmo Plot a Model's Residuals, Response, and Partial Dependence Plots | 3.6.2 | 3.6.2 |
plotrix Various Plotting Functions | 3.8-4 | 3.8-4 |
plotSEMM Graphing Nonlinear Relations Among Latent Variables from Structural Equation Mixture Models | 2.4 | 2.4 |
plRasch Log Linear by Linear Association models and Rasch family models by pseudolikelihood estimation | 1.0 | 1.0 |
pls Partial Least Squares and Principal Component Regression | 2.8-3 | 2.8-3 |
plsdof Degrees of Freedom and Statistical Inference for Partial Least Squares Regression | 0.3-0 | 0.3-0 |
plsRbeta Partial Least Squares Regression for Beta Regression Models | 0.3.0 | 0.3.0 |
plsRglm Partial Least Squares Regression for Generalized Linear Models | 1.5.1 | 1.5.1 |
plumber An API Generator for R | 1.2.1 | 1.2.1 |
plyr Tools for Splitting, Applying and Combining Data | 1.8.9 | 1.8.9 |
pmclust Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model | 0.2-1 | 0.2-1 |
PMCMR Calculate Pairwise Multiple Comparisons of Mean Rank Sums | 4.4 | 4.4 |
PMCMRplus Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended | 1.9.10 | 1.9.10 |
pmml Generate PMML for Various Models | 2.5.2 | 2.5.2 |
pmmlTransformations Transforms Input Data from a PMML Perspective | 1.3.3 | 1.3.3 |
pmr Probability Models for Ranking Data | 1.2.5.1 | 1.2.5.1 |
pmultinom One-Sided Multinomial Probabilities | 1.0.0 | 1.0.0 |
pmxTools Pharmacometric and Pharmacokinetic Toolkit | 1.3 | 1.3 |
png Read and write PNG images | 0.1-7 | 0.1-7 |
poibin The Poisson Binomial Distribution | 1.5 | 1.5 |
PoiClaClu Classification and Clustering of Sequencing Data Based on a Poisson Model | 1.0.2.1 | 1.0.2.1 |
poilog Poisson Lognormal and Bivariate Poisson Lognormal Distribution | 0.4.2 | 0.4.2 |
poisbinom A Faster Implementation of the Poisson-Binomial Distribution | 1.0.1 | 1.0.1 |
PoissonBinomial Efficient Computation of Ordinary and Generalized Poisson Binomial Distributions | 1.2.6 | 1.2.6 |
poistweedie Poisson-Tweedie Exponential Family Models | 1.0.2 | 1.0.2 |
poLCA Polytomous Variable Latent Class Analysis | 1.6.0.1 | 1.6.0.1 |
polite Be Nice on the Web | 0.1.2 | 0.1.2 |
polspline Polynomial Spline Routines | 1.1.24 | 1.1.24 |
polyaAeppli Implementation of the Polya-Aeppli Distribution | 2.0.2 | 2.0.2 |
polyclip Polygon Clipping | 1.10-6 | 1.10-6 |
polycor Polychoric and Polyserial Correlations | 0.8-1 | 0.8-1 |
polyCub Cubature over Polygonal Domains | 0.9.0 | 0.9.0 |
polyMatrix Infrastructure for Manipulation Polynomial Matrices | 0.9.16 | 0.9.16 |
polynom A Collection of Functions to Implement a Class for Univariate Polynomial Manipulations | 1.4-1 | 1.4-1 |
PolynomF Polynomials in R | 2.0-5 | 2.0-5 |
polyreg Polynomial Regression | 0.8.0 | 0.8.0 |
polysat Tools for Polyploid Microsatellite Analysis | 1.7-6 | 1.7-6 |
pomp Statistical Inference for Partially Observed Markov Processes | 5.11 | 5.11 |
pool Object Pooling | 1.0.2 | 1.0.2 |
poolr Methods for Pooling P-Values from (Dependent) Tests | 1.1-1 | 1.1-1 |
poorman A Poor Man's Dependency Free Recreation of 'dplyr' | 0.2.7 | 0.2.7 |
popbio Construction and Analysis of Matrix Population Models | 2.7 | 2.7 |
PopED Population (and Individual) Optimal Experimental Design | 0.6.0 | 0.6.0 |
popEpi Functions for Epidemiological Analysis using Population Data | 0.4.11 | 0.4.11 |
poppr Genetic Analysis of Populations with Mixed Reproduction | 2.9.3 | 2.9.3 |
PortfolioEffectHFT High Frequency Portfolio Analytics by PortfolioEffect | 1.8 | 1.8 |
PortfolioOptim Small/Large Sample Portfolio Optimization | 1.1.1 | 1.1.1 |
PortRisk Portfolio Risk Analysis | 1.1.0 | 1.1.0 |
posterior Tools for Working with Posterior Distributions | 1.5.0 | 1.5.0 |
postGIStools Tools for Interacting with 'PostgreSQL' / 'PostGIS' Databases | 0.2.4 | 0.2.4 |
postlightmercury Parses Web Pages using Postlight Mercury | 1.2 | 1.2 |
postlogic Infix and Postfix Logic Operators | 0.1.0.1 | 0.1.0.1 |
POT Generalized Pareto Distribution and Peaks Over Threshold | 1.1-10 | 1.1-10 |
PottsUtils Utility Functions of the Potts Models | 0.3-3 | 0.3-3 |
powdist Power and Reversal Power Distributions | 0.1.4 | 0.1.4 |
powerbydesign Power Estimates for ANOVA Designs | 1.0.5 | 1.0.5 |
powerGWASinteraction Power Calculations for GxE and GxG Interactions for GWAS | 1.1.3 | 1.1.3 |
poweRlaw Analysis of Heavy Tailed Distributions | 0.80.0 | 0.80.0 |
powerSurvEpi Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies | 0.1.3 | 0.1.3 |
PowerTOST Power and Sample Size for (Bio)Equivalence Studies | 1.5-4 | 1.5-4 |
PowerUpR Power Analysis Tools for Multilevel Randomized Experiments | 1.1.0 | 1.1.0 |
PP Person Parameter Estimation | 0.6.3-11 | 0.6.3-11 |
ppcor Partial and Semi-Partial (Part) Correlation | 1.1 | 1.1 |
ppmSuite A Collection of Models that Employ Product Partition Distributions as a Prior on Partitions | 0.3.4 | 0.3.4 |
pps PPS Sampling | 1.0 | 1.0 |
prabclus Functions for Clustering and Testing of Presence-Absence, Abundance and Multilocus Genetic Data | 2.3-3 | 2.3-3 |
pracma Practical Numerical Math Functions | 2.4.4 | 2.4.4 |
PracTools Designing and Weighting Survey Samples | 1.4.2 | 1.4.2 |
praise Praise Users | 1.0.0 | 1.0.0 |
prcr Person-Centered Analysis | 0.2.1 | 0.2.1 |
PreciseSums Accurate Floating Point Sums and Products | 0.6 | 0.6 |
prediction Tidy, Type-Safe 'prediction()' Methods | 0.3.14 | 0.3.14 |
preference 2-Stage Preference Trial Design and Analysis | 1.1.6 | 1.1.6 |
prefmod Utilities to Fit Paired Comparison Models for Preferences | 0.8-36 | 0.8-36 |
PReMiuM Dirichlet Process Bayesian Clustering, Profile Regression | 3.2.13 | 3.2.13 |
preprocessCore | 1.66.0 | 1.66.0 |
prereg R Markdown Templates to Preregister Scientific Studies | 0.6.0 | 0.6.0 |
PresenceAbsence Presence-Absence Model Evaluation | 1.1.11 | 1.1.11 |
presize Precision Based Sample Size Calculation | 0.3.7 | 0.3.7 |
prettycode Pretty Print R Code in the Terminal | 1.1.0 | 1.1.0 |
prettydoc Creating Pretty Documents from R Markdown | 0.4.1 | 0.4.1 |
prettyGraphs Publication-Quality Graphics | 2.1.6 | 2.1.6 |
prettyR Pretty Descriptive Stats | 2.2-3 | 2.2-3 |
prettyunits Pretty, Human Readable Formatting of Quantities | 1.2.0 | 1.2.0 |
prevalence Tools for Prevalence Assessment Studies | 0.4.1 | 0.4.1 |
prevR Estimating Regional Trends of a Prevalence from a DHS and Similar Surveys | 5.0.0 | 5.0.0 |
primefactr Use Prime Factorization for Computations | 0.1.1 | 0.1.1 |
primePCA Projected Refinement for Imputation of Missing Entries in PCA | 1.2 | 1.2 |
primes Fast Functions for Prime Numbers | 1.6.0 | 1.6.0 |
PRIMME Eigenvalues and Singular Values and Vectors from Large Matrices | 3.2-6 | 3.2-6 |
princurve Fit a Principal Curve in Arbitrary Dimension | 2.1.6 | 2.1.6 |
prism Access Data from the Oregon State Prism Climate Project | 0.2.1 | 0.2.1 |
PRISMAstatement Plot Flow Charts According to the "PRISMA" Statement | 1.1.1 | 1.1.1 |
ProbitSpatial Probit with Spatial Dependence, SAR, SEM and SARAR Models | 1.1 | 1.1 |
ProbReco Score Optimal Probabilistic Forecast Reconciliation | 0.1.2 | 0.1.2 |
pROC Display and Analyze ROC Curves | 1.18.5 | 1.18.5 |
processx Execute and Control System Processes | 3.8.3 | 3.8.3 |
prodigenr Research Project Directory Generator | 0.6.2 | 0.6.2 |
prodlim Product-Limit Estimation for Censored Event History Analysis | 2023.08.28 | 2023.08.28 |
ProfessR Grades Setting and Exam Maker | 2.4-3 | 2.4-3 |
proffer Profile R Code and Visualize with 'Pprof' | 0.1.6 | 0.1.6 |
profile Read, Manipulate, and Write Profiler Data | 1.0.3 | 1.0.3 |
ProfileLikelihood Profile Likelihood for a Parameter in Commonly Used Statistical Models | 1.2 | 1.2 |
profileModel Profiling Inference Functions for Various Model Classes | 0.6.1 | 0.6.1 |
profileR Profile Analysis of Multivariate Data in R | 0.3-5 | 0.3-5 |
profmem Simple Memory Profiling for R | 0.6.0 | 0.6.0 |
profoc Probabilistic Forecast Combination Using CRPS Learning | 1.3.1 | 1.3.1 |
proftools Profile Output Processing Tools for R | 0.99-3 | 0.99-3 |
profvis Interactive Visualizations for Profiling R Code | 0.3.8 | 0.3.8 |
progress Terminal Progress Bars | 1.2.3 | 1.2.3 |
progressr An Inclusive, Unifying API for Progress Updates | 0.14.0 | 0.14.0 |
PROJ Generic Coordinate System Transformations Using 'PROJ' | 0.4.0 | 0.4.0 |
proj4 A simple interface to the PROJ.4 cartographic projections library | 1.0-14 | 1.0-14 |
ProjectionBasedClustering Projection Based Clustering | 1.2.1 | 1.2.1 |
projects A Project Infrastructure for Researchers | 2.1.3 | 2.1.3 |
ProjectTemplate Automates the Creation of New Statistical Analysis Projects | 0.10.4 | 0.10.4 |
projpred Projection Predictive Feature Selection | 2.0.2 | 2.0.2 |
promises Abstractions for Promise-Based Asynchronous Programming | 1.2.1 | 1.2.1 |
PropClust Propensity Clustering and Decomposition | 1.4-6 | 1.4-6 |
prophet Automatic Forecasting Procedure | 1.0 | 1.0 |
propr Calculating Proportionality Between Vectors of Compositional Data | 4.2.6 | 4.2.6 |
ProtGenerics | 1.30.0 | 1.30.0 |
proto Prototype Object-Based Programming | 1.0.0 | 1.0.0 |
protoclust Hierarchical Clustering with Prototypes | 1.6.4 | 1.6.4 |
protolite Highly Optimized Protocol Buffer Serializers | 2.3.0 | 2.3.0 |
proxy Distance and Similarity Measures | 0.4-27 | 0.4-27 |
prrd Parallel Runs of Reverse Depends | 0.0.5 | 0.0.5 |
PRROC Precision-Recall and ROC Curves for Weighted and Unweighted Data | 1.3.1 | 1.3.1 |
pryr Tools for Computing on the Language | 0.1.6 | 0.1.6 |
ps List, Query, Manipulate System Processes | 1.7.6 | 1.7.6 |
psbcGroup Penalized Parametric and Semiparametric Bayesian Survival Models with Shrinkage and Grouping Priors | 1.7 | 1.7 |
pscl Political Science Computational Laboratory | 1.5.9 | 1.5.9 |
psd Adaptive, Sine-Multitaper Power Spectral Density and Cross Spectrum Estimation | 2.1.1 | 2.1.1 |
pseudo Computes Pseudo-Observations for Modeling | 1.4.3 | 1.4.3 |
pseval Methods for Evaluating Principal Surrogates of Treatment Response | 1.3.1 | 1.3.1 |
PSF Forecasting of Univariate Time Series Using the Pattern Sequence-Based Forecasting (PSF) Algorithm | 0.5 | 0.5 |
psfmi Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets | 1.4.0 | 1.4.0 |
psgp Projected Spatial Gaussian Process Methods | 0.3-19 | 0.3-19 |
psidR Build Panel Data Sets from PSID Raw Data | 2.1 | 2.1 |
pso Particle Swarm Optimization | 1.0.4 | 1.0.4 |
psoptim Particle Swarm Optimization | 1.0 | 1.0 |
pspline Penalized Smoothing Splines | 1.0-19 | 1.0-19 |
psqn Partially Separable Quasi-Newton | 0.3.1 | 0.3.1 |
PSweight Propensity Score Weighting for Causal Inference with Observational Studies and Randomized Trials | 1.1.8 | 1.1.8 |
psy Various Procedures Used in Psychometrics | 1.2 | 1.2 |
psych Procedures for Psychological, Psychometric, and Personality Research | 2.4.1 | 2.4.1 |
psychmeta Psychometric Meta-Analysis Toolkit | 2.6.5 | 2.6.5 |
psychometric Applied Psychometric Theory | 2.2 | 2.2 |
psychomix Psychometric Mixture Models | 1.1-8 | 1.1-8 |
psychonetrics Structural Equation Modeling and Confirmatory Network Analysis | 0.11.5 | 0.11.5 |
psychotools Psychometric Modeling Infrastructure | 0.7-3 | 0.7-3 |
psychotree Recursive Partitioning Based on Psychometric Models | 0.16-0 | 0.16-0 |
psymetadata Open Datasets from Meta-Analyses in Psychology Research | 1.0.1 | 1.0.1 |
psyphy Functions for Analyzing Psychophysical Data in R | 0.3 | 0.3 |
PTAk Principal Tensor Analysis on k Modes | 2.0.0 | 2.0.0 |
PTSR Positive Time Series Regression | 0.1.2 | 0.1.2 |
ptsuite Tail Index Estimation for Power Law Distributions | 1.0.0 | 1.0.0 |
ptw Parametric Time Warping | 1.9-16 | 1.9-16 |
pubh A Toolbox for Public Health and Epidemiology | 1.2.7 | 1.2.7 |
PublicationBias Sensitivity Analysis for Publication Bias in Meta-Analyses | 2.4.0 | 2.4.0 |
publipha Bayesian Meta-Analysis with Publications Bias and P-Hacking | 0.1.2 | 0.1.2 |
Publish Format Output of Various Routines in a Suitable Way for Reports and Publication | 2023.01.17 | 2023.01.17 |
pubmed.mineR Text Mining of PubMed Abstracts | 1.0.19 | 1.0.19 |
pubmedR Gathering Metadata About Publications, Grants, Clinical Trials from 'PubMed' Database | 0.0.3 | 0.0.3 |
puma | 3.40.0 | 3.40.0 |
pumadata | 2.34.0 | 2.34.0 |
puniform Meta-Analysis Methods Correcting for Publication Bias | 0.2.7 | 0.2.7 |
purrr Functional Programming Tools | 1.0.2 | 1.0.2 |
purrrogress Add Progress Bars to Mapping Functions | 0.1.1 | 0.1.1 |
pushoverr Send Push Notifications using 'Pushover' | 1.1.0 | 1.1.0 |
pvclust Hierarchical Clustering with P-Values via Multiscale Bootstrap Resampling | 2.2-0 | 2.2-0 |
pwr Basic Functions for Power Analysis | 1.3-0 | 1.3-0 |
PwrGSD Power in a Group Sequential Design | 2.3.6 | 2.3.6 |
pwrRasch Statistical Power Simulation for Testing the Rasch Model | 0.1-2 | 0.1-2 |
pwt Penn World Table (Versions 5.6, 6.x, 7.x) | 7.1-1 | 7.1-1 |
pwt8 Penn World Table (Version 8.x) | 8.1-1 | 8.1-1 |
pwt9 Penn World Table (Version 9.x) | 9.1-0 | 9.1-0 |
pxweb R Interface to PXWEB APIs | 0.17.0 | 0.17.0 |
PxWebApiData PX-Web Data by API | 0.9.0 | 0.9.0 |
pyinit Pena-Yohai Initial Estimator for Robust S-Regression | 1.1.3 | 1.1.3 |
qap Heuristics for the Quadratic Assignment Problem (QAP) | 0.1-2 | 0.1-2 |
qcc Quality Control Charts | 2.7 | 2.7 |
qcv Quantifying Construct Validity | 1.0 | 1.0 |
qdap Bridging the Gap Between Qualitative Data and Quantitative Analysis | 2.4.6 | 2.4.6 |
qdapDictionaries Dictionaries and Word Lists for the 'qdap' Package | 1.0.7 | 1.0.7 |
qdapRegex Regular Expression Removal, Extraction, and Replacement Tools | 0.7.8 | 0.7.8 |
qdapTools Tools for the 'qdap' Package | 1.3.7 | 1.3.7 |
qgam Smooth Additive Quantile Regression Models | 1.3.4 | 1.3.4 |
qgraph Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation | 1.9.8 | 1.9.8 |
qgtools Generalized Quantitative Genetics Data Analyses | 2.0 | 2.0 |
qicharts2 Quality Improvement Charts | 0.7.4 | 0.7.4 |
qlcMatrix Utility Sparse Matrix Functions for Quantitative Language Comparison | 0.9.7 | 0.9.7 |
qmap Statistical Transformations for Post-Processing Climate Model Output | 1.0-4 | 1.0-4 |
qMRI Methods for Quantitative Magnetic Resonance Imaging ('qMRI') | 1.2.7 | 1.2.7 |
qpdf Split, Combine and Compress PDF Files | 1.3.2 | 1.3.2 |
qpmadr Interface to the 'qpmad' Quadratic Programming Solver | 1.1.0-0 | 1.1.0-0 |
QPmin Linearly Constrained Indefinite Quadratic Program Solver | 0.5-1 | 0.5-1 |
qpNCA Noncompartmental Pharmacokinetic Analysis by qPharmetra | 1.1.6 | 1.1.6 |
qqconf Creates Simultaneous Testing Bands for QQ-Plots | 1.3.2 | 1.3.2 |
qqman Q-Q and Manhattan Plots for GWAS Data | 0.1.9 | 0.1.9 |
qqplotr Quantile-Quantile Plot Extensions for 'ggplot2' | 0.0.6 | 0.0.6 |
qqr Data from Brazilian Soccer Championship | 0.0.1 | 0.0.1 |
QRM Provides R-Language Code to Examine Quantitative Risk Management Concepts | 0.4-31 | 0.4-31 |
qrmdata Data Sets for Quantitative Risk Management Practice | 2022-05-31-1 | 2022-05-31-1 |
qrmtools Tools for Quantitative Risk Management | 0.0-16 | 0.0-16 |
qrng (Randomized) Quasi-Random Number Generators | 0.0-10 | 0.0-10 |
qs Quick Serialization of R Objects | 0.25.7 | 0.25.7 |
qspray Multivariate Polynomials with Rational Coefficients | 3.1.0 | 3.1.0 |
qsub Running Commands Remotely on 'Gridengine' Clusters | 1.1.3 | 1.1.3 |
qte Quantile Treatment Effects | 1.3.1 | 1.3.1 |
qtl Tools for Analyzing QTL Experiments | 1.66 | 1.66 |
qtlDesign Design of QTL (Quantitative Trait Locus) Experiments | 0.941 | 0.941 |
QTLRel Tools for Mapping of Quantitative Traits of Genetically Related Individuals and Calculating Identity Coefficients from Pedigrees | 1.14 | 1.14 |
QTOCen Quantile-Optimal Treatment Regimes with Censored Data | 0.1.1 | 0.1.1 |
Qtools Utilities for Quantiles | 1.5.6 | 1.5.6 |
quadform Efficient Evaluation of Quadratic Forms | 0.0-2 | 0.0-2 |
quadprog Functions to Solve Quadratic Programming Problems | 1.5-8 | 1.5-8 |
quadprogXT Quadratic Programming with Absolute Value Constraints | 0.0.5 | 0.0.5 |
qualmap Opinionated Approach for Digitizing Semi-Structured Qualitative GIS Data | 0.2.2 | 0.2.2 |
qualtRics Download 'Qualtrics' Survey Data | 3.2.0 | 3.2.0 |
qualV Qualitative Validation Methods | 0.3-5 | 0.3-5 |
QUALYPSO Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections | 2.3 | 2.3 |
Quandl API Wrapper for Quandl.com | 2.11.0 | 2.11.0 |
quantdr Dimension Reduction Techniques for Conditional Quantiles | 1.2.2 | 1.2.2 |
quanteda Quantitative Analysis of Textual Data | 3.3.1 | 3.3.1 |
quantification Quantification of Qualitative Survey Data | 0.2.0 | 0.2.0 |
quantmod Quantitative Financial Modelling Framework | 0.4.25 | 0.4.25 |
quantoptr Algorithms for Quantile- And Mean-Optimal Treatment Regimes | 0.1.3 | 0.1.3 |
quantreg Quantile Regression | 5.97 | 5.97 |
quantregForest Quantile Regression Forests | 1.3-7 | 1.3-7 |
quantregGrowth Non-Crossing Additive Regression Quantiles and Non-Parametric Growth Charts | 1.7-0 | 1.7-0 |
quantspec Quantile-Based Spectral Analysis of Time Series | 1.2-3 | 1.2-3 |
quantstrat | 0.16.9 | 0.16.9 |
questionr Functions to Make Surveys Processing Easier | 0.7.7 | 0.7.7 |
QuickJSR Interface for the 'QuickJS' Lightweight 'JavaScript' Engine | 1.1.3 | 1.1.3 |
quickmapr Quickly Map and Explore Spatial Data | 0.3.0 | 0.3.0 |
quickPlot A System of Plotting Optimized for Speed and Modularity | 0.1.8 | 0.1.8 |
quickpsy Fits Psychometric Functions for Multiple Groups | 0.1.5.1 | 0.1.5.1 |
quint Qualitative Interaction Trees | 2.2.2 | 2.2.2 |
qvalue Q-value estimation for false discovery rate control | 2.36.0 | 2.36.0 |
qvcalc Quasi Variances for Factor Effects in Statistical Models | 1.0.3 | 1.0.3 |
R.cache Fast and Light-Weight Caching (Memoization) of Objects and Results to Speed Up Computations | 0.16.0 | 0.16.0 |
R.devices Unified Handling of Graphics Devices | 2.17.2 | 2.17.2 |
R.matlab Read and Write MAT Files and Call MATLAB from Within R | 3.7.0 | 3.7.0 |
R.methodsS3 S3 Methods Simplified | 1.8.2 | 1.8.2 |
R.oo R Object-Oriented Programming with or without References | 1.26.0 | 1.26.0 |
R.rsp Dynamic Generation of Scientific Reports | 0.44.0 | 0.44.0 |
R.utils Various Programming Utilities | 2.12.3 | 2.12.3 |
R2admb 'ADMB' to R Interface Functions | 0.7.16.3 | 0.7.16.3 |
R2BEAT Multistage Sampling Allocation and Sample Selection | 1.0.5 | 1.0.5 |
r2d2 Bivariate (Two-Dimensional) Confidence Region and Frequency Distribution | 1.0-0 | 1.0-0 |
r2d3 Interface to 'D3' Visualizations | 0.2.6 | 0.2.6 |
R2HTML HTML Exportation for R Objects | 2.3.3 | 2.3.3 |
R2jags Using R to Run 'JAGS' | 0.7-1 | 0.7-1 |
r2mlm R-Squared Measures for Multilevel Models | 0.3.3 | 0.3.3 |
R2OpenBUGS Running OpenBUGS from R | 3.2-3.2.1 | 3.2-3.2.1 |
r2rtf Easily Create Production-Ready Rich Text Format (RTF) Table and Figure | 1.1.1 | 1.1.1 |
R2WinBUGS Running 'WinBUGS' and 'OpenBUGS' from 'R' / 'S-PLUS' | 2.1-22 | 2.1-22 |
R4CouchDB A R Convenience Layer for CouchDB 2.0 | 0.7.5 | 0.7.5 |
R6 Encapsulated Classes with Reference Semantics | 2.5.1 | 2.5.1 |
ra4bayesmeta Reference Analysis for Bayesian Meta-Analysis | 1.0-8 | 1.0-8 |
RaceID Identification of Cell Types, Inference of Lineage Trees, and Prediction of Noise Dynamics from Single-Cell RNA-Seq Data | 0.2.4 | 0.2.4 |
radarchart Radar Chart from 'Chart.js' | 0.3.1 | 0.3.1 |
RadData Nuclear Decay Data for Dosimetric Calculations - ICRP 107 | 1.0.1 | 1.0.1 |
radiant Business Analytics using R and Shiny | 1.5.0 | 1.5.0 |
radiant.basics Basics Menu for Radiant: Business Analytics using R and Shiny | 1.6.0 | 1.6.0 |
radiant.data Data Menu for Radiant: Business Analytics using R and Shiny | 1.6.3 | 1.6.3 |
radiant.design Design Menu for Radiant: Business Analytics using R and Shiny | 1.6.1 | 1.6.1 |
radiant.model Model Menu for Radiant: Business Analytics using R and Shiny | 1.6.3 | 1.6.3 |
radiant.multivariate Multivariate Menu for Radiant: Business Analytics using R and Shiny | 1.6.1 | 1.6.1 |
radsafer Radiation Safety | 2.3.0 | 2.3.0 |
RAdwords Loading Google Adwords Data into R | 0.1.18 | 0.1.18 |
ragg Graphic Devices Based on AGG | 1.2.5 | 1.2.5 |
ragtop Pricing Equity Derivatives with Extensions of Black-Scholes | 1.1.1 | 1.1.1 |
rainbow Bagplots, Boxplots and Rainbow Plots for Functional Data | 3.8 | 3.8 |
rakeR Easy Spatial Microsimulation (Raking) in R | 0.2.1 | 0.2.1 |
rAmCharts JavaScript Charts Tool | 2.1.15 | 2.1.15 |
ramcmc Robust Adaptive Metropolis Algorithm | 0.1.2 | 0.1.2 |
ramify Additional Matrix Functionality | 0.3.3 | 0.3.3 |
ramps Bayesian Geostatistical Modeling with RAMPS | 0.6.18 | 0.6.18 |
RandMeta Efficient Numerical Algorithm for Exact Inference in Meta Analysis | 0.1.0 | 0.1.0 |
randNames Package Provides Access to Fake User Data | 0.2.3 | 0.2.3 |
random True Random Numbers using RANDOM.ORG | 0.2.6 | 0.2.6 |
RandomFields Simulation and Analysis of Random Fields | 3.3.14 | 3.3.14 |
RandomFieldsUtils Utilities for the Simulation and Analysis of Random Fields and Genetic Data | 1.1.0 | 1.1.0 |
randomForest Breiman and Cutlers Random Forests for Classification and Regression | 4.7-1.1 | 4.7-1.1 |
randomForestSRC Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) | 3.2.3 | 3.2.3 |
randomGLM Random General Linear Model Prediction | 1.02-1 | 1.02-1 |
randomizr Easy-to-Use Tools for Common Forms of Random Assignment and Sampling | 1.0.0 | 1.0.0 |
randomLCA Random Effects Latent Class Analysis | 1.1-3 | 1.1-3 |
randtoolbox Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests | 2.0.4 | 2.0.4 |
RandVar Implementation of Random Variables | 1.2.3 | 1.2.3 |
ranger A Fast Implementation of Random Forests | 0.15.1 | 0.15.1 |
rankdist Distance Based Ranking Models | 1.1.4 | 1.1.4 |
rankhazard Rank-Hazard Plots | 1.1.0 | 1.1.0 |
RANN Fast Nearest Neighbour Search (Wraps ANN Library) Using L2 Metric | 2.6.1 | 2.6.1 |
rapiclient Dynamic OpenAPI/Swagger Client | 0.1.3 | 0.1.3 |
rapidjsonr 'Rapidjson' C++ Header Files | 1.2.0 | 1.2.0 |
rapidoc Generates 'RapiDoc' Documentation from an 'OpenAPI' Specification | 9.3.4 | 9.3.4 |
RApiSerialize R API Serialization | 0.1.2 | 0.1.2 |
rappdirs Application Directories: Determine Where to Save Data, Caches, and Logs | 0.3.3 | 0.3.3 |
rapport A Report Templating System | 1.1 | 1.1 |
rapportools Miscellaneous (Stats) Helper Functions with Sane Defaults for Reporting | 1.1 | 1.1 |
rARPACK Solvers for Large Scale Eigenvalue and SVD Problems | 0.11-0 | 0.11-0 |
RaschSampler Rasch Sampler | 0.8-10 | 0.8-10 |
raster Geographic Data Analysis and Modeling | 3.6-26 | 3.6-26 |
rasterImage An Improved Wrapper of image() | 0.4.0 | 0.4.0 |
rasterVis Visualization Methods for Raster Data | 0.51.6 | 0.51.6 |
ratelimitr Rate Limiting for R | 0.4.1 | 0.4.1 |
ratesci Confidence Intervals for Comparisons of Binomial or Poisson Rates | 0.4-0 | 0.4-0 |
RATest Randomization Tests | 0.1.10 | 0.1.10 |
RationalMatrix Exact Matrix Algebra for Rational Matrices | 1.0.0 | 1.0.0 |
ratioOfQsprays Fractions of Multivariate Polynomials with Rational Coefficients | 1.1.0 | 1.1.0 |
raveio File-System Toolbox for RAVE Project | 0.0.8 | 0.0.8 |
RavenR Raven Hydrological Modelling Framework R Support and Analysis | 2.2.0 | 2.2.0 |
ravetools Signal and Image Processing Toolbox for Analyzing Intracranial Electroencephalography Data | 0.1.8 | 0.1.8 |
rBayesianOptimization Bayesian Optimization of Hyperparameters | 1.2.0 | 1.2.0 |
Rbeast Bayesian Change-Point Detection and Time Series Decomposition | 0.9.9 | 0.9.9 |
rbenchmark Benchmarking routine for R | 1.0.0 | 1.0.0 |
RBesT R Bayesian Evidence Synthesis Tools | 1.7-3 | 1.7-3 |
rbibutils Read 'Bibtex' Files and Convert Between Bibliography Formats | 2.2.16 | 2.2.16 |
Rblpapi R Interface to 'Bloomberg' | 0.3.13 | 0.3.13 |
rbokeh R Interface for Bokeh | 0.5.2 | 0.5.2 |
Rborist Extensible, Parallelizable Implementation of the Random Forest Algorithm | 0.3-7 | 0.3-7 |
RBtest Regression-Based Approach for Testing the Type of Missing Data | 1.1 | 1.1 |
rbundler Rbundler manages an application's dependencies systematically and repeatedly. | 0.3.7 | 0.3.7 |
RCAL Regularized Calibrated Estimation | 2.0 | 2.0 |
Rcapture Loglinear Models for Capture-Recapture Experiments | 1.4-4 | 1.4-4 |
RCarb Dose Rate Modelling of Carbonate-Rich Samples | 0.1.6 | 0.1.6 |
rcartocolor 'CARTOColors' Palettes | 2.1.1 | 2.1.1 |
RCassandra R/Cassandra interface | 0.1-3 | 0.1-3 |
Rcatch22 Calculation of 22 Canonical Time-Series Characteristics | 0.2.1 | 0.2.1 |
rcbalance Large, Sparse Optimal Matching with Refined Covariate Balance | 1.8.8 | 1.8.8 |
rcbsubset Optimal Subset Matching with Refined Covariate Balance | 1.1.7 | 1.1.7 |
rcdd Computational Geometry | 1.6 | 1.6 |
rcdk Interface to the 'CDK' Libraries | 3.8.1 | 3.8.1 |
rcdklibs The CDK Libraries Packaged for R | 2.8 | 2.8 |
RCEIM R Cross Entropy Inspired Method for Optimization | 0.3 | 0.3 |
Rcgmin Conjugate Gradient Minimization of Nonlinear Functions with Box Constraints | 2022-4.30 | 2022-4.30 |
rchess Chess Move, Generation/Validation, Piece Placement/ Movement, and Check/Checkmate/Stalemate Detection | 0.1 | 0.1 |
Rchoice Discrete Choice (Binary, Poisson and Ordered) Models with Random Parameters | 0.3-6 | 0.3-6 |
rCMA R-to-Java Interface for 'CMA-ES' | 1.1.1 | 1.1.1 |
rcmdcheck Run 'R CMD check' from 'R' and Capture Results | 1.4.0 | 1.4.0 |
Rcmdr R Commander | 2.9-0 | 2.9-0 |
RcmdrMisc R Commander Miscellaneous Functions | 2.9-1 | 2.9-1 |
RcmdrPlugin.DoE R Commander Plugin for (Industrial) Design of Experiments | 0.12-5 | 0.12-5 |
RcmdrPlugin.EZR R Commander Plug-in for the EZR (Easy R) Package | 1.63 | 1.63 |
RcmdrPlugin.MA Graphical User Interface for Conducting Meta-Analyses in R | 0.0-2 | 0.0-2 |
RcmdrPlugin.RMTCJags R MTC Jags 'Rcmdr' Plugin | 1.0-2 | 1.0-2 |
RcmdrPlugin.sampling Tools for sampling in Official Statistical Surveys | 1.1 | 1.1 |
RcmdrPlugin.temis Graphical Integrated Text Mining Solution | 0.7.10 | 0.7.10 |
RColorBrewer ColorBrewer Palettes | 1.1-3 | 1.1-3 |
rcoreoa Client for the CORE API | 0.4.0 | 0.4.0 |
Rcpp Seamless R and C++ Integration | 1.0.12 | 1.0.12 |
RcppAlgos High Performance Tools for Combinatorics and Computational Mathematics | 2.8.3 | 2.8.3 |
RcppAnnoy 'Rcpp' Bindings for 'Annoy', a Library for Approximate Nearest Neighbors | 0.0.22 | 0.0.22 |
RcppAPT 'Rcpp' Interface to the APT Package Manager | 0.0.9 | 0.0.9 |
RcppArmadillo 'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library | 0.12.6.6.1 | 0.12.6.6.1 |
RcppBigIntAlgos Factor Big Integers with the Parallel Quadratic Sieve | 1.1.0 | 1.1.0 |
RcppCCTZ 'Rcpp' Bindings for the 'CCTZ' Library | 0.2.12 | 0.2.12 |
RcppCGAL 'Rcpp' Integration for 'CGAL' | 5.6.4 | 5.6.4 |
RcppDate 'date' C++ Header Library for Date and Time Functionality | 0.0.3 | 0.0.3 |
RcppDE Global Optimization by Differential Evolution in C++ | 0.1.7 | 0.1.7 |
RcppDist 'Rcpp' Integration of Additional Probability Distributions | 0.1.1 | 0.1.1 |
RcppDL Deep Learning Methods via Rcpp | 0.0.5 | 0.0.5 |
RcppEigen 'Rcpp' Integration for the 'Eigen' Templated Linear Algebra Library | 0.3.3.9.4 | 0.3.3.9.4 |
RcppEnsmallen Header-Only C++ Mathematical Optimization Library for 'Armadillo' | 0.2.21.0.1 | 0.2.21.0.1 |
RcppGSL 'Rcpp' Integration for 'GNU GSL' Vectors and Matrices | 0.3.13 | 0.3.13 |
RcppHNSW 'Rcpp' Bindings for 'hnswlib', a Library for Approximate Nearest Neighbors | 0.5.0 | 0.5.0 |
RcppMLPACK 'Rcpp' Integration for the 'MLPACK' Library | 1.0.10-7 | 1.0.10-7 |
RcppNumerical 'Rcpp' Integration for Numerical Computing Libraries | 0.6-0 | 0.6-0 |
RcppParallel Parallel Programming Tools for 'Rcpp' | 5.1.7 | 5.1.7 |
RcppProgress An Interruptible Progress Bar with OpenMP Support for C++ in R Packages | 0.4.2 | 0.4.2 |
RcppQuantuccia R Bindings to the Calendaring Functionality of 'QuantLib' | 0.1.2 | 0.1.2 |
RcppRedis 'Rcpp' Bindings for 'Redis' using the 'hiredis' Library | 0.2.4 | 0.2.4 |
RcppRoll Efficient Rolling / Windowed Operations | 0.3.0 | 0.3.0 |
RcppSimdJson 'Rcpp' Bindings for the 'simdjson' Header-Only Library for 'JSON' Parsing | 0.1.11 | 0.1.11 |
RcppThread R-Friendly Threading in C++ | 2.1.6 | 2.1.6 |
rcpptimer 'Rcpp' Tic-Toc Timer with 'OpenMP' Support | 1.2.1 | 1.2.1 |
RcppTOML 'Rcpp' Bindings to Parser for "Tom's Obvious Markup Language" | 0.2.2 | 0.2.2 |
RcppZiggurat 'Rcpp' Integration of Different "Ziggurat" Normal RNG Implementations | 0.1.6 | 0.1.6 |
Rcrawler Web Crawler and Scraper | 0.1.9-1 | 0.1.9-1 |
rcrossref Client for Various 'CrossRef' 'APIs' | 1.2.0 | 1.2.0 |
Rcsdp R Interface to the CSDP Semidefinite Programming Library | 0.1.57.5 | 0.1.57.5 |
RCurl General Network (HTTP/FTP/...) Client Interface for R | 1.98-1.14 | 1.98-1.14 |
RCzechia Spatial Objects of the Czech Republic | 1.12.0 | 1.12.0 |
rdatacite Client for the 'DataCite' API | 0.5.4 | 0.5.4 |
rdbnomics Download DBnomics Data | 0.6.4 | 0.6.4 |
rdd Regression Discontinuity Estimation | 0.57 | 0.57 |
rddensity Manipulation Testing Based on Density Discontinuity | 2.5 | 2.5 |
rdflib Tools to Manipulate and Query Semantic Data | 0.2.5 | 0.2.5 |
rdhs API Client and Dataset Management for the Demographic and Health Survey (DHS) Data | 0.8.1 | 0.8.1 |
RDieHarder R Interface to the 'DieHarder' RNG Test Suite | 0.2.6 | 0.2.6 |
rdlocrand Local Randomization Methods for RD Designs | 1.0 | 1.0 |
rdmulti Analysis of RD Designs with Multiple Cutoffs or Scores | 1.1 | 1.1 |
RDota2 An R Steam API Client for Valve's Dota2 | 0.1.6 | 0.1.6 |
Rdpack Update and Manipulate Rd Documentation Objects | 2.6 | 2.6 |
rdpower Power Calculations for RD Designs | 2.2 | 2.2 |
rdrobust Robust Data-Driven Statistical Inference in Regression-Discontinuity Designs | 2.2 | 2.2 |
rdrop2 Programmatic Interface to the 'Dropbox' API | 0.8.2.1 | 0.8.2.1 |
Rdsdp R Interface to DSDP Semidefinite Programming Library | 1.0.5.2.1 | 1.0.5.2.1 |
Rdsm Threads Environment for R | 2.1.1 | 2.1.1 |
rdwd Select and Download Climate Data from 'DWD' (German Weather Service) | 1.8.0 | 1.8.0 |
re2 R Interface to Google RE2 (C++) Regular Expression Library | 0.1.2 | 0.1.2 |
reactable Interactive Data Tables for R | 0.4.4 | 0.4.4 |
reactR React Helpers | 0.5.0 | 0.5.0 |
ReacTran Reactive Transport Modelling in 1d, 2d and 3d | 1.4.3.1 | 1.4.3.1 |
read.gt3x Parse 'ActiGraph' 'GT3X'/'GT3X+' 'Accelerometer' Data | 1.2.0 | 1.2.0 |
readabs Download and Tidy Time Series Data from the Australian Bureau of Statistics | 0.4.14 | 0.4.14 |
readbitmap Simple Unified Interface to Read Bitmap Images (BMP,JPEG,PNG,TIFF) | 0.1.5 | 0.1.5 |
readBrukerFlexData Reads Mass Spectrometry Data in Bruker *flex Format | 1.9.2 | 1.9.2 |
reader Suite of Functions to Flexibly Read Data from Files | 1.0.6 | 1.0.6 |
readJDX Import Data in the JCAMP-DX Format | 0.6.4 | 0.6.4 |
readMzXmlData Reads Mass Spectrometry Data in mzXML Format | 2.8.3 | 2.8.3 |
readODS Read and Write ODS Files | 2.1.0 | 2.1.0 |
readr Read Rectangular Text Data | 2.1.5 | 2.1.5 |
readsdmx Read SDMX-XML Data | 0.3.1 | 0.3.1 |
readstata13 Import 'Stata' Data Files | 0.10.1 | 0.10.1 |
readxl Read Excel Files | 1.4.3 | 1.4.3 |
REBayes Empirical Bayes Estimation and Inference | 2.54 | 2.54 |
rebmix Finite Mixture Modeling, Clustering & Classification | 2.15.0 | 2.15.0 |
recipes Preprocessing and Feature Engineering Steps for Modeling | 1.0.9 | 1.0.9 |
reclin Record Linkage Toolkit | 0.1.2 | 0.1.2 |
recmap Compute the Rectangular Statistical Cartogram | 1.0.17 | 1.0.17 |
RecordLinkage Record Linkage Functions for Linking and Deduplicating Data Sets | 0.4-12.4 | 0.4-12.4 |
recurse Computes Revisitation Metrics for Trajectory Data | 1.2.0 | 1.2.0 |
reda Recurrent Event Data Analysis | 0.5.4 | 0.5.4 |
redcapAPI Interface to 'REDCap' | 2.10.0 | 2.10.0 |
REDCapR Interaction Between R and REDCap | 1.1.0 | 1.1.0 |
redist Simulation Methods for Legislative Redistricting | 4.0.1 | 4.0.1 |
redistmetrics Redistricting Metrics | 1.0.2 | 1.0.2 |
redland RDF Library Bindings in R | 1.0.17-17 | 1.0.17-17 |
redux R Bindings to 'hiredis' | 1.1.4 | 1.1.4 |
refineR Reference Interval Estimation using Real-World Data | 1.6.2 | 1.6.2 |
RefManageR Straightforward 'BibTeX' and 'BibLaTeX' Bibliography Management | 1.4.0 | 1.4.0 |
reformulas Machinery for Processing Random Effect Formulas | 0.3.0 | 0.3.0 |
refund Regression with Functional Data | 0.1-34 | 0.1-34 |
regda Regularised Discriminant Analysis | 1.0 | 1.0 |
regions Processing Regional Statistics | 0.1.8 | 0.1.8 |
registr Curve Registration for Exponential Family Functional Data | 2.1.0 | 2.1.0 |
registry Infrastructure for R Package Registries | 0.5-1 | 0.5-1 |
regmedint Regression-Based Causal Mediation Analysis with Interaction and Effect Modification Terms | 1.0.1 | 1.0.1 |
regress Gaussian Linear Models with Linear Covariance Structure | 1.3-21 | 1.3-21 |
regsem Regularized Structural Equation Modeling | 1.9.5 | 1.9.5 |
regspec Non-Parametric Bayesian Spectrum Estimation for Multirate Data | 2.7 | 2.7 |
regtools Regression and Classification Tools | 1.7.0 | 1.7.0 |
ReIns Functions from "Reinsurance: Actuarial and Statistical Aspects" | 1.0.14 | 1.0.14 |
reinsureR Reinsurance Treaties Application | 0.1.0 | 0.1.0 |
relations Data Structures and Algorithms for Relations | 0.6-13 | 0.6-13 |
reldist Relative Distribution Methods | 1.7-2 | 1.7-2 |
relimp Relative Contribution of Effects in a Regression Model | 1.0-5 | 1.0-5 |
relliptical The Truncated Elliptical Family of Distributions | 1.2.0 | 1.2.0 |
relsurv Relative Survival | 2.2-8 | 2.2-8 |
rema Rare Event Meta Analysis | 0.0.1 | 0.0.1 |
rematch Match Regular Expressions with a Nicer 'API' | 2.0.0 | 2.0.0 |
rematch2 Tidy Output from Regular Expression Matching | 2.1.2 | 2.1.2 |
remotes R Package Installation from Remote Repositories, Including 'GitHub' | 2.4.2.1 | 2.4.2.1 |
REndo Fitting Linear Models with Endogenous Regressors using Latent Instrumental Variables | 2.4.10 | 2.4.10 |
Renext Renewal Method for Extreme Values Extrapolation | 3.1-4 | 3.1-4 |
rentrez 'Entrez' in R | 1.2.3 | 1.2.3 |
renv Project Environments | 1.0.11 | 1.0.11 |
replicateBE Average Bioequivalence with Expanding Limits (ABEL) | 1.1.3 | 1.1.3 |
repo A Data-Centered Data Flow Manager | 2.1.5 | 2.1.5 |
RepoGenerator Generates a Project and Repo for Easy Initialization of a Workshop | 0.0.1 | 0.0.1 |
report Automated Reporting of Results and Statistical Models | 0.5.1 | 0.5.1 |
reportfactory Lightweight Infrastructure for Handling Multiple R Markdown Documents | 0.4.0 | 0.4.0 |
reportr A General Message and Error Reporting System | 1.3.0 | 1.3.0 |
reporttools Generate "LaTeX"" Tables of Descriptive Statistics | 1.1.3 | 1.1.3 |
repr Serializable Representations | 1.1.6 | 1.1.6 |
represtools Reproducible Research Tools | 0.1.3 | 0.1.3 |
reprex Prepare Reproducible Example Code via the Clipboard | 2.1.0 | 2.1.0 |
reproducible Enhance Reproducibility of R Code | 2.0.10 | 2.0.10 |
reproj Coordinate System Transformations for Generic Map Data | 0.4.3 | 0.4.3 |
repurrrsive Examples of Recursive Lists and Nested or Split Data Frames | 1.0.0 | 1.0.0 |
reqres Powerful Classes for HTTP Requests and Responses | 0.2.5 | 0.2.5 |
request High Level 'HTTP' Client | 0.1.0 | 0.1.0 |
Require Installing and Loading R Packages for Reproducible Workflows | 0.3.1 | 0.3.1 |
RERconverge | 0.1.0 | 0.1.0 |
rerddap General Purpose Client for 'ERDDAP' Servers | 1.1.0 | 1.1.0 |
rerddapXtracto Extracts Environmental Data from 'ERDDAP™' Web Services | 1.2.0 | 1.2.0 |
reReg Recurrent Event Regression | 1.4.6 | 1.4.6 |
resampledata Data Sets for Mathematical Statistics with Resampling in R | 0.3.1 | 0.3.1 |
reservoir Tools for Analysis, Design, and Operation of Water Supply Storages | 1.1.5 | 1.1.5 |
reshape Flexibly Reshape Data | 0.8.9 | 0.8.9 |
reshape2 Flexibly Reshape Data: A Reboot of the Reshape Package | 1.4.4 | 1.4.4 |
ResourceSelection Resource Selection (Probability) Functions for Use-Availability Data | 0.3-5 | 0.3-5 |
restfulr R Interface to RESTful Web Services | 0.0.15 | 0.0.15 |
restimizeapi Functions for Working with the 'www.estimize.com' Web Services | 1.0.0 | 1.0.0 |
restriktor Restricted Statistical Estimation and Inference for Linear Models | 0.5-90 | 0.5-90 |
RestRserve A Framework for Building HTTP API | 1.2.1 | 1.2.1 |
reticulate Interface to 'Python' | 1.35.0 | 1.35.0 |
retroharmonize Ex Post Survey Data Harmonization | 0.2.0 | 0.2.0 |
retrosheet Import Professional Baseball Data from 'Retrosheet' | 1.1.5 | 1.1.5 |
retry Repeated Evaluation | 0.1.0 | 0.1.0 |
revdbayes Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis | 1.5.3 | 1.5.3 |
revealjs R Markdown Format for 'reveal.js' Presentations | 0.9 | 0.9 |
revss Robust Estimation in Very Small Samples | 1.0.5 | 1.0.5 |
revtools Tools to Support Evidence Synthesis | 0.4.1 | 0.4.1 |
rex Friendly Regular Expressions | 1.2.1 | 1.2.1 |
Rexperigen R Interface to Experigen | 0.2.1 | 0.2.1 |
Rfacebook Access to Facebook API via R | 0.6.15 | 0.6.15 |
Rfast A Collection of Efficient and Extremely Fast R Functions | 2.1.0 | 2.1.0 |
Rfast2 A Collection of Efficient and Extremely Fast R Functions II | 0.1.5.1 | 0.1.5.1 |
rFerns Random Ferns Classifier | 5.0.0 | 5.0.0 |
rfigshare An R Interface to 'figshare' | 0.3.8 | 0.3.8 |
Rfit Rank-Based Estimation for Linear Models | 0.24.2 | 0.24.2 |
rflexscan The Flexible Spatial Scan Statistic | 1.1.0 | 1.1.0 |
rgbif Interface to the Global Biodiversity Information Facility API | 3.7.9 | 3.7.9 |
rgdal Bindings for the 'Geospatial' Data Abstraction Library | 1.6-7 | 1.6-7 |
rgee R Bindings for Calling the 'Earth Engine' API | 1.1.7 | 1.1.7 |
rgen Random Sampling Distribution C++ Routines for Armadillo | 0.0.1 | 0.0.1 |
RGENERATE Tools to Generate Vector Time Series | 1.3.7 | 1.3.7 |
rgenoud R Version of GENetic Optimization Using Derivatives | 5.9-0.10 | 5.9-0.10 |
rgeoda R Library for Spatial Data Analysis | 0.0.10-4 | 0.0.10-4 |
rgeolocate IP Address Geolocation | 1.4.2 | 1.4.2 |
rgeos Interface to Geometry Engine - Open Source ('GEOS') | 0.6-3 | 0.6-3 |
RGF Regularized Greedy Forest | 1.1.1 | 1.1.1 |
rgl 3D Visualization Using OpenGL | 1.2.8 | 1.2.8 |
Rglpk R/GNU Linear Programming Kit Interface | 0.6-5.1 | 0.6-5.1 |
rglwidget 'rgl' in 'htmlwidgets' Framework | 0.2.1 | 0.2.1 |
RgoogleMaps Overlays on Static Maps | 1.5.1 | 1.5.1 |
rgrass7 Deprecated Interface Between GRASS Geographical Information System and R | 0.2-13 | 0.2-13 |
RGreenplum Interface to 'Greenplum' Database | 0.1.2 | 0.1.2 |
rgugik Search and Retrieve Spatial Data from 'GUGiK' | 0.4.0 | 0.4.0 |
RH2 DBI/RJDBC Interface to H2 Database | 0.2.4 | 0.2.4 |
rhandsontable Interface to the 'Handsontable.js' Library | 0.3.8 | 0.3.8 |
RHMS Hydrologic Modelling System for R Users | 1.7 | 1.7 |
rhosa Higher-Order Spectral Analysis | 0.2.0 | 0.2.0 |
rhosp Side Effect Risks in Hospital : Simulation and Estimation | 1.10 | 1.10 |
RhpcBLASctl Control the Number of Threads on 'BLAS' | 0.23-42 | 0.23-42 |
Rhtslib | 2.0.0 | 2.0.0 |
rhub Tools for R Package Developers | 1.1.2 | 1.1.2 |
RIbench Benchmark Suite for Indirect Methods for RI Estimation | 1.0.2 | 1.0.2 |
riingo An R Interface to the 'Tiingo' Stock Price API | 0.3.1 | 0.3.1 |
Rilostat ILO Open Data via Ilostat Bulk Download Facility | 1.1.8 | 1.1.8 |
rim Interface to 'Maxima', Enabling Symbolic Computation | 0.6.4 | 0.6.4 |
ring Circular / Ring Buffers | 1.0.4 | 1.0.4 |
RInside C++ Classes to Embed R in C++ (and C) Applications | 0.2.18 | 0.2.18 |
rio A Swiss-Army Knife for Data I/O | 0.5.30 | 0.5.30 |
rioja Analysis of Quaternary Science Data | 1.0-6 | 1.0-6 |
Rirt Data Analysis and Parameter Estimation Using Item Response Theory | 0.0.2 | 0.0.2 |
Risk Computes 26 Financial Risk Measures for Any Continuous Distribution | 1.0 | 1.0 |
riskCommunicator G-Computation to Estimate Interpretable Epidemiological Effects | 1.0.1 | 1.0.1 |
riskParityPortfolio Design of Risk Parity Portfolios | 0.2.2 | 0.2.2 |
RiskPortfolios Computation of Risk-Based Portfolios | 2.1.7 | 2.1.7 |
riskRegression Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks | 2023.12.21 | 2023.12.21 |
risksetROC Riskset ROC Curve Estimation from Censored Survival Data | 1.0.4.1 | 1.0.4.1 |
riskSimul Risk Quantification for Stock Portfolios under the T-Copula Model | 0.1.2 | 0.1.2 |
riverdist River Network Distance Computation and Applications | 0.16.3 | 0.16.3 |
rivernet Read, Analyze and Plot River Networks | 1.2.3 | 1.2.3 |
rjags Bayesian Graphical Models using MCMC | 4-12 | 4-12 |
rJava Low-Level R to Java Interface | 1.0-11 | 1.0-11 |
RJDBC Provides Access to Databases Through the JDBC Interface | 0.2-10 | 0.2-10 |
RJDemetra Interface to 'JDemetra+' Seasonal Adjustment Software | 0.2.4 | 0.2.4 |
rje Miscellaneous Useful Functions for Statistics | 1.12.1 | 1.12.1 |
rjson JSON for R | 0.2.21 | 0.2.21 |
RJSONIO Serialize R Objects to JSON, JavaScript Object Notation | 1.3-1.9 | 1.3-1.9 |
rjstat Handle 'JSON-stat' Format in R | 0.4.3 | 0.4.3 |
RKEA R/KEA Interface | 0.0-6 | 0.0-6 |
RKEAjars R/KEA Interface Jars | 5.0-4 | 5.0-4 |
RKelly Translate Odds and Probabilities | 1.0 | 1.0 |
rlang Functions for Base Types and Core R and 'Tidyverse' Features | 1.1.3 | 1.1.3 |
rlas Read and Write 'las' and 'laz' Binary File Formats Used for Remote Sensing Data | 1.7.0 | 1.7.0 |
rle Common Functions for Run-Length Encoded Vectors | 0.9.2 | 0.9.2 |
rlecuyer R Interface to RNG with Multiple Streams | 0.3-8 | 0.3-8 |
rlemon R Access to LEMON Graph Algorithms | 0.2.1 | 0.2.1 |
Rlgt Bayesian Exponential Smoothing Models with Trend Modifications | 0.2-1 | 0.2-1 |
Rlibeemd Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN) | 1.4.3 | 1.4.3 |
rLiDAR LiDAR Data Processing and Visualization | 0.1.5 | 0.1.5 |
Rlinkedin Access to the LinkedIn API via R | 0.2 | 0.2 |
Rlinsolve Iterative Solvers for (Sparse) Linear System of Equations | 0.3.2 | 0.3.2 |
rlist A Toolbox for Non-Tabular Data Manipulation | 0.4.6.2 | 0.4.6.2 |
rlme Rank-Based Estimation and Prediction in Random Effects Nested Models | 0.5 | 0.5 |
RLRsim Exact (Restricted) Likelihood Ratio Tests for Mixed and Additive Models | 3.1-8 | 3.1-8 |
RLT Reinforcement Learning Trees | 3.2.6 | 3.2.6 |
rLTP R Interface to the 'LTP'-Cloud Service | 0.1.4 | 0.1.4 |
RLumShiny 'Shiny' Applications for the R Package 'Luminescence' | 0.2.3 | 0.2.3 |
RM2006 RiskMetrics 2006 Methodology | 0.1.1 | 0.1.1 |
rma.exact Exact Confidence Intervals for Random Effects Meta-Analyses | 0.1.0 | 0.1.0 |
Rmagic MAGIC - Markov Affinity-Based Graph Imputation of Cells | 2.0.3 | 2.0.3 |
Rmalschains Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) | 0.2-10 | 0.2-10 |
rmapshaper Client for 'mapshaper' for 'Geospatial' Operations | 0.4.6 | 0.4.6 |
RMariaDB Database Interface and MariaDB Driver | 1.3.2 | 1.3.2 |
rmarkdown Dynamic Documents for R | 2.25 | 2.25 |
rmatio Read and Write 'Matlab' Files | 0.16.0 | 0.16.0 |
RMAWGEN Multi-Site Auto-Regressive Weather GENerator | 1.3.7 | 1.3.7 |
RmdConcord Concordances for 'R Markdown' | 0.3 | 0.3 |
rmdfiltr 'Lua'-Filters for R Markdown | 0.1.5 | 0.1.5 |
rmdformats HTML Output Formats and Templates for 'rmarkdown' Documents | 1.0.4 | 1.0.4 |
rmdpartials Partial 'rmarkdown' Documents to Prettify your Reports | 0.5.8 | 0.5.8 |
rmeta Meta-Analysis | 3.0 | 3.0 |
rmgarch Multivariate GARCH Models | 1.3-9 | 1.3-9 |
rMIDAS Multiple Imputation with Denoising Autoencoders | 1.0.0 | 1.0.0 |
rminer Data Mining Classification and Regression Methods | 1.4.6 | 1.4.6 |
rminizinc R Interface to 'MiniZinc' | 0.0.8 | 0.0.8 |
rmio Provides 'mio' C++11 Header Files | 0.4.0 | 0.4.0 |
Rmisc Ryan Miscellaneous | 1.5.1 | 1.5.1 |
Rmixmod Classification with Mixture Modelling | 2.1.10 | 2.1.10 |
RMixtComp Mixture Models with Heterogeneous and (Partially) Missing Data | 4.1.4 | 4.1.4 |
RMixtCompIO Minimal Interface of the C++ 'MixtComp' Library for Mixture Models with Heterogeneous and (Partially) Missing Data | 4.0.11 | 4.0.11 |
RMixtCompUtilities Utility Functions for 'MixtComp' Outputs | 4.1.6 | 4.1.6 |
RMKdiscrete Sundry Discrete Probability Distributions | 0.2 | 0.2 |
rmoo Multi-Objective Optimization in R | 0.2.0 | 0.2.0 |
Rmosek The R to MOSEK Optimization Interface | 1.3.5 | 1.3.5 |
Rmpfr R MPFR - Multiple Precision Floating-Point Reliable | 0.9-5 | 0.9-5 |
Rmpi Interface (Wrapper) to MPI (Message-Passing Interface) | 0.7-2 | 0.7-2 |
rms Regression Modeling Strategies | 6.7-1 | 6.7-1 |
RMTstat Distributions, Statistics and Tests Derived from Random Matrix Theory | 0.3.1 | 0.3.1 |
rmutil Utilities for Nonlinear Regression and Repeated Measurements Models | 1.1.10 | 1.1.10 |
RMySQL Database Interface and 'MySQL' Driver for R | 0.10.27 | 0.10.27 |
Rnanoflann Extremely Fast Nearest Neighbor Search | 0.0.2 | 0.0.2 |
RNAseqNet Log-Linear Poisson Graphical Model with Hot-Deck Multiple Imputation | 0.1.4 | 0.1.4 |
rnaturalearth World Map Data from Natural Earth | 1.0.1 | 1.0.1 |
rnaturalearthdata World Vector Map Data from Natural Earth Used in 'rnaturalearth' | 0.1.0 | 0.1.0 |
RNCEP Obtain, Organize, and Visualize NCEP Weather Data | 1.0.10 | 1.0.10 |
rncl An Interface to the Nexus Class Library | 0.8.7 | 0.8.7 |
RND Risk Neutral Density Extraction Package | 1.2 | 1.2 |
rneos XML-RPC Interface to NEOS | 0.4-0 | 0.4-0 |
RNetCDF Interface to 'NetCDF' Datasets | 2.9-1 | 2.9-1 |
RNeXML Semantically Rich I/O for the 'NeXML' Format | 2.4.11 | 2.4.11 |
rngtools Utility Functions for Working with Random Number Generators | 1.5.2 | 1.5.2 |
rngWELL Toolbox for WELL Random Number Generators | 0.10-10 | 0.10-10 |
RNifti Fast R and C++ Access to NIfTI Images | 1.5.1 | 1.5.1 |
Rniftilib Rniftilib - R Interface to NIFTICLIB (V2.0.0: 2010-07-20) | 0.0-35 | 0.0-35 |
RNiftyReg Image Registration Using the 'NiftyReg' Library | 2.8.1 | 2.8.1 |
rnmamod Bayesian Network Meta-Analysis with Missing Participants | 0.3.0 | 0.3.0 |
rnn Recurrent Neural Network | 1.9.0 | 1.9.0 |
rnoaa 'NOAA' Weather Data from R | 1.4.0 | 1.4.0 |
rnrfa UK National River Flow Archive Data from R | 2.1.0 | 2.1.0 |
roadoi Find Free Versions of Scholarly Publications via Unpaywall | 0.7.2 | 0.7.2 |
ROAuth R Interface For OAuth | 0.9.6 | 0.9.6 |
RobAStBase Robust Asymptotic Statistics | 1.2.3 | 1.2.3 |
robCompositions Compositional Data Analysis | 2.4.1 | 2.4.1 |
robcor Robust Correlations | 0.1-6.1 | 0.1-6.1 |
robeth R Functions for Robust Statistics | 2.7-8 | 2.7-8 |
robets Forecasting Time Series with Robust Exponential Smoothing | 1.4 | 1.4 |
robfilter Robust Time Series Filters | 4.1.4 | 4.1.4 |
RobKF Innovative and/or Additive Outlier Robust Kalman Filtering | 1.0.2 | 1.0.2 |
RoBMA Robust Bayesian Meta-Analyses | 3.1.0 | 3.1.0 |
robmixglm Robust Generalized Linear Models (GLM) using Mixtures | 1.2-3 | 1.2-3 |
robotstxt A 'robots.txt' Parser and 'Webbot'/'Spider'/'Crawler' Permissions Checker | 0.7.13 | 0.7.13 |
RobPer Robust Periodogram and Periodicity Detection Methods | 1.2.3 | 1.2.3 |
RobRex Optimally Robust Influence Curves for Regression and Scale | 1.2.0 | 1.2.0 |
RobRSVD Robust Regularized Singular Value Decomposition | 1.0 | 1.0 |
RobStatTM Robust Statistics: Theory and Methods | 1.0.11 | 1.0.11 |
robsurvey Robust Survey Statistics Estimation | 0.6 | 0.6 |
robumeta Robust Variance Meta-Regression | 2.1 | 2.1 |
robust Port of the S+ "Robust Library" | 0.7-3 | 0.7-3 |
RobustAFT Truncated Maximum Likelihood Fit and Robust Accelerated Failure Time Regression for Gaussian and Log-Weibull Case | 1.4-7 | 1.4-7 |
robustarima Robust ARIMA Modeling | 0.2.6 | 0.2.6 |
robustbase Basic Robust Statistics | 0.99-2 | 0.99-2 |
RobustBayesianCopas Robust Bayesian Copas Selection Model | 2.0 | 2.0 |
robustDA Robust Mixture Discriminant Analysis | 1.2 | 1.2 |
robustgam Robust Estimation for Generalized Additive Models | 0.1.7 | 0.1.7 |
robustHD Robust Methods for High-Dimensional Data | 0.8.0 | 0.8.0 |
robustlmm Robust Linear Mixed Effects Models | 3.3-1 | 3.3-1 |
robustrank Robust Rank-Based Tests | 2024.1-28 | 2024.1-28 |
RobustRankAggreg Methods for Robust Rank Aggregation | 1.2.1 | 1.2.1 |
robustrao An Extended Rao-Stirling Diversity Index to Handle Missing Data | 1.0-5 | 1.0-5 |
robustreg Robust Regression Functions | 0.1-11 | 0.1-11 |
robustX 'eXtra' / 'eXperimental' Functionality for Robust Statistics | 1.2-7 | 1.2-7 |
rockchalk Regression Estimation and Presentation | 1.8.157 | 1.8.157 |
rocker Database Interface Class | 0.3.1 | 0.3.1 |
ROCR Visualizing the Performance of Scoring Classifiers | 1.0-11 | 1.0-11 |
RODBC ODBC Database Access | 1.3-23 | 1.3-23 |
rodd Optimal Discriminating Designs | 0.2-1 | 0.2-1 |
rODE Ordinary Differential Equation (ODE) Solvers Written in R Using S4 Classes | 0.99.6 | 0.99.6 |
rodeo A Code Generator for ODE-Based Models | 0.7.8 | 0.7.8 |
ROI R Optimization Infrastructure | 1.0-1 | 1.0-1 |
ROI.plugin.neos 'NEOS' Plug-in for the 'R' Optimization Interface | 1.0-2 | 1.0-2 |
ROI.plugin.qpoases 'qpOASES' Plugin for the 'R' Optimization Infrastructure | 1.0-3 | 1.0-3 |
roll Rolling and Expanding Statistics | 1.1.6 | 1.1.6 |
ROOPSD R Object Oriented Programming for Statistical Distribution | 0.3.9 | 0.3.9 |
RootsExtremaInflections Finds Roots, Extrema and Inflection Points of a Curve | 1.2.1 | 1.2.1 |
rootSolve Nonlinear Root Finding, Equilibrium and Steady-State Analysis of Ordinary Differential Equations | 1.8.2.4 | 1.8.2.4 |
ROpenDota Access OpenDota Services in R | 0.1.2 | 0.1.2 |
roperators Additional Operators to Help you Write Cleaner R Code | 1.2.0 | 1.2.0 |
ROptEst Optimally Robust Estimation | 1.3.1 | 1.3.1 |
ROptEstOld Optimally Robust Estimation - Old Version | 1.2.0 | 1.2.0 |
roptim General Purpose Optimization in R using C++ | 0.1.6 | 0.1.6 |
ROptRegTS Optimally Robust Estimation for Regression-Type Models | 1.2.0 | 1.2.0 |
ROptSpace Matrix Reconstruction from a Few Entries | 0.2.3 | 0.2.3 |
rorcid Interface to the 'Orcid.org' API | 0.7.0 | 0.7.0 |
ROSE Random Over-Sampling Examples | 0.0-4 | 0.0-4 |
rosetteApi 'Rosette' API | 1.14.4 | 1.14.4 |
rosm Plot Raster Map Tiles from Open Street Map and Other Sources | 0.3.0 | 0.3.0 |
rospca Robust Sparse PCA using the ROSPCA Algorithm | 1.0.4 | 1.0.4 |
RoughSets Data Analysis Using Rough Set and Fuzzy Rough Set Theories | 1.3-8 | 1.3-8 |
routr A Simple Router for HTTP and WebSocket Requests | 0.4.1 | 0.4.1 |
roxygen2 In-Line Documentation for R | 7.3.1 | 7.3.1 |
rpact Confirmatory Adaptive Clinical Trial Design and Analysis | 3.5.0 | 3.5.0 |
rpanel Simple Interactive Controls for R using the 'tcltk' Package | 1.1-5.2 | 1.1-5.2 |
rpart Recursive Partitioning and Regression Trees | 4.1.23 | 4.1.23 |
rpart.plot Plot 'rpart' Models: An Enhanced Version of 'plot.rpart' | 3.1.1 | 3.1.1 |
rpatrec Recognising Visual Charting Patterns in Time Series Data | 1.0.1 | 1.0.1 |
rpca RobustPCA: Decompose a Matrix into Low-Rank and Sparse Components | 0.2.3 | 0.2.3 |
rpdo Pacific Decadal Oscillation Index Data | 0.3.2 | 0.3.2 |
rpf Response Probability Functions | 1.0.14 | 1.0.14 |
Rphylopars Phylogenetic Comparative Tools for Missing Data and Within-Species Variation | 0.3.10 | 0.3.10 |
rpinterest Access Pinterest API | 0.3.1 | 0.3.1 |
rplos Interface to the Search API for 'PLoS' Journals | 1.0.0 | 1.0.0 |
RPMG Graphical User Interface (GUI) for Interactive R Analysis Sessions | 2.2-7 | 2.2-7 |
RPMM Recursively Partitioned Mixture Model | 1.25 | 1.25 |
rpms Recursive Partitioning for Modeling Survey Data | 0.5.1 | 0.5.1 |
rpostgis R Interface to a 'PostGIS' Database | 1.5.1 | 1.5.1 |
RPostgres C++ Interface to PostgreSQL | 1.4.6 | 1.4.6 |
RPostgreSQL R Interface to the 'PostgreSQL' Database System | 0.7-6 | 0.7-6 |
RPPairwiseDesign Resolvable partially pairwise balanced design and Space-filling design via association scheme | 1.0 | 1.0 |
RPresto DBI Connector to Presto | 1.4.6 | 1.4.6 |
rprintf Adaptive Builder for Formatted Strings | 0.2.1 | 0.2.1 |
rprojroot Finding Files in Project Subdirectories | 2.0.4 | 2.0.4 |
RProtoBuf R Interface to the 'Protocol Buffers' 'API' (Version 2 or 3) | 0.4.22 | 0.4.22 |
rpsftm Rank Preserving Structural Failure Time Models | 1.2.8 | 1.2.8 |
RPtests Goodness of Fit Tests for High-Dimensional Linear Regression Models | 0.1.5 | 0.1.5 |
RPushbullet R Interface to the Pushbullet Messaging Service | 0.3.4 | 0.3.4 |
RPyGeo ArcGIS Geoprocessing via Python | 1.0.0 | 1.0.0 |
rpymat Easy to Configure an Isolated 'Python' Environment | 0.1.7 | 0.1.7 |
rqdatatable 'rquery' for 'data.table' | 1.3.1 | 1.3.1 |
rqPen Penalized Quantile Regression | 3.2.1 | 3.2.1 |
RQuantLib R Interface to the 'QuantLib' Library | 0.4.20 | 0.4.20 |
rquery Relational Query Generator for Data Manipulation at Scale | 1.4.9 | 1.4.9 |
rrcov Scalable Robust Estimators with High Breakdown Point | 1.7-5 | 1.7-5 |
rrcovHD Robust Multivariate Methods for High Dimensional Data | 0.2-7 | 0.2-7 |
rrcovNA Scalable Robust Estimators with High Breakdown Point for Incomplete Data | 0.5-0 | 0.5-0 |
rrefine r Client for OpenRefine API | 2.1.0 | 2.1.0 |
rrlda Robust Regularized Linear Discriminant Analysis | 1.1 | 1.1 |
rromeo Access Publisher Copyright & Self-Archiving Policies via the 'SHERPA/RoMEO' API | 0.1.1 | 0.1.1 |
RRPP Linear Model Evaluation with Randomized Residuals in a Permutation Procedure | 1.4.0 | 1.4.0 |
RRreg Correlation and Regression Analyses for Randomized Response Data | 0.7.5 | 0.7.5 |
RRTCS Randomized Response Techniques for Complex Surveys | 0.0.4 | 0.0.4 |
rrum Bayesian Estimation of the Reduced Reparameterized Unified Model with Gibbs Sampling | 0.2.1 | 0.2.1 |
rsae Robust Small Area Estimation | 0.2 | 0.2 |
RSAGA SAGA Geoprocessing and Terrain Analysis | 1.3.0 | 1.3.0 |
Rsagacmd Linking R with the Open-Source 'SAGA-GIS' Software | 0.4.2 | 0.4.2 |
RSAlgaeR Builds Empirical Remote Sensing Models of Water Quality Variables and Analyzes Long-Term Trends | 1.0.0 | 1.0.0 |
rsample General Resampling Infrastructure | 1.2.0 | 1.2.0 |
Rsamtools | 2.14.0 | 2.14.0 |
rsatscan Tools, Classes, and Methods for Interfacing with 'SaTScan' Stand-Alone Software | 0.3.9200 | 0.3.9200 |
rsconnect Deploy Docs, Apps, and APIs to 'Posit Connect', 'shinyapps.io', and 'RPubs' | 0.8.25 | 0.8.25 |
rscopus Scopus Database 'API' Interface | 0.6.6 | 0.6.6 |
rsdmx Tools for Reading SDMX Data and Metadata | 0.6 | 0.6 |
RSEIS Seismic Time Series Analysis Tools | 4.1-6 | 4.1-6 |
RSelenium R Bindings for 'Selenium WebDriver' | 1.7.9 | 1.7.9 |
rsem Robust Structural Equation Modeling with Missing Data and Auxiliary Variables | 0.5.1 | 0.5.1 |
Rserve Binary R server | 1.8-13 | 1.8-13 |
Rsfar Seasonal Functional Autoregressive Models | 0.0.1 | 0.0.1 |
RSGHB Functions for Hierarchical Bayesian Estimation: A Flexible Approach | 1.2.2 | 1.2.2 |
RSiteCatalyst R Client for Adobe Analytics API V1.4 | 1.4.16 | 1.4.16 |
RSKC Robust Sparse K-Means | 2.4.2 | 2.4.2 |
rslurm Submit R Calculations to a 'Slurm' Cluster | 0.6.2 | 0.6.2 |
rsm Response-Surface Analysis | 2.10.4 | 2.10.4 |
RSmartlyIO Loading Facebook and Instagram Advertising Data from 'Smartly.io' | 0.1.3 | 0.1.3 |
RSNNS Neural Networks using the Stuttgart Neural Network Simulator (SNNS) | 0.4-17 | 0.4-17 |
rsoi Import Various Northern and Southern Hemisphere Climate Indices | 0.5.6 | 0.5.6 |
Rsolnp General Non-Linear Optimization | 1.16 | 1.16 |
rspa Adapt Numerical Records to Fit (in)Equality Restrictions | 0.2.8 | 0.2.8 |
rsparse Statistical Learning on Sparse Matrices | 0.5.1 | 0.5.1 |
RSpectra Solvers for Large-Scale Eigenvalue and SVD Problems | 0.16-1 | 0.16-1 |
RSQLite SQLite Interface for R | 2.3.5 | 2.3.5 |
Rssa A Collection of Methods for Singular Spectrum Analysis | 1.0.5 | 1.0.5 |
rstan R Interface to Stan | 2.32.5 | 2.32.5 |
rstanarm Bayesian Applied Regression Modeling via Stan | 2.26.1 | 2.26.1 |
rstantools Tools for Developing R Packages Interfacing with 'Stan' | 2.4.0 | 2.4.0 |
rstatix Pipe-Friendly Framework for Basic Statistical Tests | 0.7.2 | 0.7.2 |
rstiefel Random Orthonormal Matrix Generation and Optimization on the Stiefel Manifold | 1.0.1 | 1.0.1 |
RStoolbox Remote Sensing Data Analysis | 0.3.0 | 0.3.0 |
rstpm2 Smooth Survival Models, Including Generalized Survival Models | 1.6.3 | 1.6.3 |
rstream Streams of Random Numbers | 1.3.7 | 1.3.7 |
RStripe A Convenience Interface for the Stripe Payment API | 0.1 | 0.1 |
rstudioapi Safely Access the RStudio API | 0.15.0 | 0.15.0 |
rsurface Design of Rotatable Central Composite Experiments and Response Surface Analysis | 1.1.0 | 1.1.0 |
RSurveillance Design and Analysis of Disease Surveillance Activities | 0.2.1 | 0.2.1 |
rsvd Randomized Singular Value Decomposition | 1.0.5 | 1.0.5 |
rsvg Render SVG Images into PDF, PNG, (Encapsulated) PostScript, or Bitmap Arrays | 2.6.0 | 2.6.0 |
Rsymphony SYMPHONY in R | 0.1-33 | 0.1-33 |
Rtauchen Discretization of AR(1) Processes | 1.0 | 1.0 |
RTDE Robust Tail Dependence Estimation | 0.2-1 | 0.2-1 |
rtdists Response Time Distributions | 0.11-5 | 0.11-5 |
rTensor Tools for Tensor Analysis and Decomposition | 1.4.8 | 1.4.8 |
rtf Rich Text Format (RTF) Output | 0.4-14.1 | 0.4-14.1 |
rticles Article Formats for R Markdown | 0.26 | 0.26 |
rtkore 'STK++' Core Library Integration to 'R' using 'Rcpp' | 1.6.10 | 1.6.10 |
RTL Risk Tool Library - Trading, Risk, 'Analytics' for Commodities | 1.3.5 | 1.3.5 |
Rtnmin Truncated Newton Function Minimization with Bounds Constraints | 2016-7.7 | 2016-7.7 |
rtop Interpolation of Data with Variable Spatial Support | 0.6-8 | 0.6-8 |
rtracklayer | 1.58.0 | 1.58.0 |
RTransferEntropy Measuring Information Flow Between Time Series with Shannon and Renyi Transfer Entropy | 0.2.21 | 0.2.21 |
rtrek Data Analysis Relating to Star Trek | 0.3.3 | 0.3.3 |
rtrim Trends and Indices for Monitoring Data | 2.1.1 | 2.1.1 |
rts Raster Time Series Analysis | 1.1-14 | 1.1-14 |
Rtsne T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation | 0.17 | 0.17 |
Rttf2pt1 'ttf2pt1' Program | 1.3.12 | 1.3.12 |
rtweet Collecting Twitter Data | 1.2.1 | 1.2.1 |
rucrdtw R Bindings for the UCR Suite | 0.1.6 | 0.1.6 |
rugarch Univariate GARCH Models | 1.5-1 | 1.5-1 |
ruimtehol Learn Text 'Embeddings' with 'Starspace' | 0.3.1 | 0.3.1 |
runexp Softball Run Expectancy using Markov Chains and Simulation | 0.2.1 | 0.2.1 |
RUnit R Unit Test Framework | 0.4.32 | 0.4.32 |
runjags Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS | 2.2.2-1.1 | 2.2.2-1.1 |
runner Running Operations for Vectors | 0.4.3 | 0.4.3 |
runstats Fast Computation of Running Statistics for Time Series | 1.1.0 | 1.1.0 |
Runuran R Interface to the 'UNU.RAN' Random Variate Generators | 0.38 | 0.38 |
rust Ratio-of-Uniforms Simulation with Transformation | 1.4.2 | 1.4.2 |
Rvcg Manipulations of Triangular Meshes Based on the 'VCGLIB' API | 0.22.2 | 0.22.2 |
rversions Query 'R' Versions, Including 'r-release' and 'r-oldrel' | 2.1.2 | 2.1.2 |
rvest Easily Harvest (Scrape) Web Pages | 1.0.3 | 1.0.3 |
rvg R Graphics Devices for 'Office' Vector Graphics Output | 0.2.5 | 0.2.5 |
Rvmmin Variable Metric Nonlinear Function Minimization | 2018-4.17.1 | 2018-4.17.1 |
Rwave Time-Frequency Analysis of 1-D Signals | 2.6-5 | 2.6-5 |
RWeka R/Weka Interface | 0.4-46 | 0.4-46 |
RWekajars R/Weka Interface Jars | 3.9.3-2 | 3.9.3-2 |
RWiener Wiener Process Distribution Functions | 1.3-3 | 1.3-3 |
rworldmap Mapping Global Data | 1.3-8 | 1.3-8 |
rworldxtra Country boundaries at high resolution. | 1.01 | 1.01 |
rwt 'Rice Wavelet Toolbox' Wrapper | 1.0.2 | 1.0.2 |
rwunderground R Interface to Weather Underground API | 0.1.8 | 0.1.8 |
RxCEcolInf 'R x C Ecological Inference With Optional Incorporation of Survey Information' | 0.1-5 | 0.1-5 |
RxODE Facilities for Simulating from ODE-Based Models | 1.1.5 | 1.1.5 |
RXshrink Maximum Likelihood Shrinkage using Generalized Ridge or Least Angle Regression | 2.3 | 2.3 |
Ryacas R Interface to the 'Yacas' Computer Algebra System | 1.1.5 | 1.1.5 |
Ryacas0 Legacy 'Ryacas' (Interface to 'Yacas' Computer Algebra System) | 0.4.4 | 0.4.4 |
RYandexTranslate R Interface to Yandex Translate API | 1.0 | 1.0 |
s2 Spherical Geometry Operators Using the S2 Geometry Library | 1.1.6 | 1.1.6 |
s20x Functions for University of Auckland Course STATS 201/208 Data Analysis | 3.1-40 | 3.1-40 |
S2sls Spatial Two Stage Least Squares Estimation | 0.1 | 0.1 |
S4Arrays | 1.4.1 | 1.4.1 |
S4Vectors | 0.42.1 | 0.42.1 |
SACOBRA Self-Adjusting COBRA | 1.2 | 1.2 |
sadists Some Additional Distributions | 0.2.5 | 0.2.5 |
sae Small Area Estimation | 1.3 | 1.3 |
saemix Stochastic Approximation Expectation Maximization (SAEM) Algorithm | 3.2 | 3.2 |
saeRobust Robust Small Area Estimation | 0.4.0 | 0.4.0 |
saeSim Simulation Tools for Small Area Estimation | 0.11.0 | 0.11.0 |
SAEval Small Area Estimation Evaluation | 1.0.0 | 1.0.0 |
safetensors Safetensors File Format | 0.1.2 | 0.1.2 |
samon Sensitivity Analysis for Missing Data | 4.0.1 | 4.0.1 |
SamplerCompare A Framework for Comparing the Performance of MCMC Samplers | 1.3.4 | 1.3.4 |
sampleSelection Sample Selection Models | 1.2-12 | 1.2-12 |
samplesize Sample Size Calculation for Various t-Tests and Wilcoxon-Test | 0.2-4 | 0.2-4 |
sampling Survey Sampling | 2.10 | 2.10 |
SamplingBigData Sampling Methods for Big Data | 1.0.0 | 1.0.0 |
samplingbook Survey Sampling Procedures | 1.2.4 | 1.2.4 |
SamplingStrata Optimal Stratification of Sampling Frames for Multipurpose Sampling Surveys | 1.5-4 | 1.5-4 |
samplingVarEst Sampling Variance Estimation | 1.5 | 1.5 |
sandwich Robust Covariance Matrix Estimators | 3.1-0 | 3.1-0 |
sanic Solving Ax = b Nimbly in C++ | 0.0.2 | 0.0.2 |
sanon Stratified Analysis with Nonparametric Covariable Adjustment | 1.6 | 1.6 |
santaR Short Asynchronous Time-Series Analysis | 1.2.3 | 1.2.3 |
SAScii Import ASCII Files Directly into R using Only a 'SAS' Input Script | 1.0.2 | 1.0.2 |
sass Syntactically Awesome Style Sheets ('Sass') | 0.4.8 | 0.4.8 |
satellite Handling and Manipulating Remote Sensing Data | 1.0.4 | 1.0.4 |
SAVER Single-Cell RNA-Seq Gene Expression Recovery | 1.1.2 | 1.1.2 |
saws Small-Sample Adjustments for Wald Tests Using Sandwich Estimators | 0.9-7.0 | 0.9-7.0 |
sazedR Parameter-Free Domain-Agnostic Season Length Detection in Time Series | 2.0.2 | 2.0.2 |
sbgcop Semiparametric Bayesian Gaussian Copula Estimation and Imputation | 0.980 | 0.980 |
sbm Stochastic Blockmodels | 0.4.6 | 0.4.6 |
sbw Stable Balancing Weights for Causal Inference and Missing Data | 1.1.5 | 1.1.5 |
sca Simple Component Analysis | 0.9-2 | 0.9-2 |
ScaledMatrix | 1.12.0 | 1.12.0 |
scales Scale Functions for Visualization | 1.3.0 | 1.3.0 |
scalreg Scaled Sparse Linear Regression | 1.0.1 | 1.0.1 |
scam Shape Constrained Additive Models | 1.2-15 | 1.2-15 |
SCAT Summary Based Conditional Association Test | 0.5.0 | 0.5.0 |
scatterD3 D3 JavaScript Scatterplot from R | 1.0.1 | 1.0.1 |
scattermore Scatterplots with More Points | 1.1 | 1.1 |
scatterpie Scatter Pie Plot | 0.2.4 | 0.2.4 |
scatterplot3d 3D Scatter Plot | 0.3-44 | 0.3-44 |
SCBmeanfd Simultaneous Confidence Bands for the Mean of Functional Data | 1.2.2 | 1.2.2 |
SCEPtER Stellar CharactEristics Pisa Estimation gRid | 0.2-4 | 0.2-4 |
SCEPtERbinary Stellar CharactEristics Pisa Estimation gRid for Binary Systems | 0.1-1 | 0.1-1 |
schoolmath Functions and Datasets for Math Used in School | 0.4.1 | 0.4.1 |
schumaker Schumaker Shape-Preserving Spline | 1.2.1 | 1.2.1 |
SCI Standardized Climate Indices Such as SPI, SRI or SPEI | 1.0-2 | 1.0-2 |
SCMA Single-Case Meta-Analysis | 1.3.1 | 1.3.1 |
scModels Fitting Discrete Distribution Models to Count Data | 1.0.4 | 1.0.4 |
SCOR Spherically Constrained Optimization Routine | 1.1.1 | 1.1.1 |
scorecardModelUtils Credit Scorecard Modelling Utils | 0.0.1.0 | 0.0.1.0 |
scoringRules Scoring Rules for Parametric and Simulated Distribution Forecasts | 1.1.1 | 1.1.1 |
scoringutils Utilities for Scoring and Assessing Predictions | 1.2.2 | 1.2.2 |
scran | 1.32.0 | 1.32.0 |
scs Splitting Conic Solver | 3.2.4 | 3.2.4 |
sctransform Variance Stabilizing Transformations for Single Cell UMI Data | 0.4.1 | 0.4.1 |
scuttle | 1.14.0 | 1.14.0 |
sda Shrinkage Discriminant Analysis and CAT Score Variable Selection | 1.3.8 | 1.3.8 |
SDaA Sampling: Design and Analysis | 0.1-5 | 0.1-5 |
sdcHierarchies Create and (Interactively) Modify Nested Hierarchies | 0.21.0 | 0.21.0 |
sdcMicro Statistical Disclosure Control Methods for Anonymization of Data and Risk Estimation | 5.7.7 | 5.7.7 |
sdcSpatial Statistical Disclosure Control for Spatial Data | 0.5.2 | 0.5.2 |
sdcTable Methods for Statistical Disclosure Control in Tabular Data | 0.32.6 | 0.32.6 |
SDD Serial Dependence Diagrams | 1.2 | 1.2 |
sde Simulation and Inference for Stochastic Differential Equations | 2.0.18 | 2.0.18 |
SDLfilter Filtering and Assessing the Sample Size of Tracking Data | 2.3.3 | 2.3.3 |
SDMTools Species Distribution Modelling Tools: Tools for processing data associated with species distribution modelling exercises | 1.1-221.2 | 1.1-221.2 |
sdpt3r Semi-Definite Quadratic Linear Programming Solver | 0.3 | 0.3 |
seacarb Seawater Carbonate Chemistry | 3.3.2 | 3.3.2 |
searchConsoleR Google Search Console R Client | 0.4.0 | 0.4.0 |
seas Seasonal Analysis and Graphics, Especially for Climatology | 0.6-0 | 0.6-0 |
season Seasonal Analysis of Health Data | 0.3.15 | 0.3.15 |
seasonal R Interface to X-13-ARIMA-SEATS | 1.10.0 | 1.10.0 |
seasonalview Graphical User Interface for Seasonal Adjustment | 0.3 | 0.3 |
seastests Seasonality Tests | 0.15.4 | 0.15.4 |
secr Spatially Explicit Capture-Recapture | 4.6.4 | 4.6.4 |
secretbase Cryptographic Hash, Extendable-Output and Base64 Functions | 1.0.3 | 1.0.3 |
see Model Visualisation Toolbox for 'easystats' and 'ggplot2' | 0.7.1 | 0.7.1 |
seer Feature-Based Forecast Model Selection | 1.1.8 | 1.1.8 |
seewave Sound Analysis and Synthesis | 2.2.0 | 2.2.0 |
seg Measuring Spatial Segregation | 0.5-7 | 0.5-7 |
segclust2d Bivariate Segmentation/Clustering Methods and Tools | 0.3.1 | 0.3.1 |
segmented Regression Models with Break-Points / Change-Points Estimation (with Possibly Random Effects) | 2.0-2 | 2.0-2 |
SEL Semiparametric Elicitation | 1.0-4 | 1.0-4 |
selectMeta Estimation of Weight Functions in Meta Analysis | 1.0.8 | 1.0.8 |
selectr Translate CSS Selectors to XPath Expressions | 0.4-2 | 0.4-2 |
seleniumPipes R Client Implementing the W3C WebDriver Specification | 0.3.7 | 0.3.7 |
sem Structural Equation Models | 3.1-13 | 3.1-13 |
semds Structural Equation Multidimensional Scaling | 0.9-6 | 0.9-6 |
SemiCompRisks Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data | 3.4 | 3.4 |
SemiMarkov Multi-States Semi-Markov Models | 1.4.6 | 1.4.6 |
seminr Building and Estimating Structural Equation Models | 2.3.1 | 2.3.1 |
SemiPar Semiparametic Regression | 1.0-4.2 | 1.0-4.2 |
SemNeT Methods and Measures for Semantic Network Analysis | 1.4.3 | 1.4.3 |
semPlot Path Diagrams and Visual Analysis of Various SEM Packages' Output | 1.1.6 | 1.1.6 |
semsfa Semiparametric Estimation of Stochastic Frontier Models | 1.1 | 1.1 |
semTools Useful Tools for Structural Equation Modeling | 0.5-5 | 0.5-5 |
semtree Recursive Partitioning for Structural Equation Models | 0.9.18 | 0.9.18 |
semver 'Semantic Versioning V2.0.0' Parser | 0.2.0 | 0.2.0 |
sendmailR Send Email Using R | 1.4-0 | 1.4-0 |
sensitivity Global Sensitivity Analysis of Model Outputs and Importance Measures | 1.30.0 | 1.30.0 |
SensoMineR Sensory Data Analysis | 1.27 | 1.27 |
sentencepiece Text Tokenization using Byte Pair Encoding and Unigram Modelling | 0.2.3 | 0.2.3 |
SentimentAnalysis Dictionary-Based Sentiment Analysis | 1.3-4 | 1.3-4 |
separationplot Separation Plots | 1.4 | 1.4 |
seqDesign Simulation and Group Sequential Monitoring of Randomized Two-Stage Treatment Efficacy Trials with Time-to-Event Endpoints | 1.2 | 1.2 |
seqinr Biological Sequences Retrieval and Analysis | 4.2-36 | 4.2-36 |
seriation Infrastructure for Ordering Objects Using Seriation | 1.5.4 | 1.5.4 |
seroincidence Estimating Infection Rates from Serological Data | 2.0.0 | 2.0.0 |
servr A Simple HTTP Server to Serve Static Files or Dynamic Documents | 0.28 | 0.28 |
sessioninfo R Session Information | 1.2.2 | 1.2.2 |
set6 R6 Mathematical Sets Interface | 0.2.4 | 0.2.4 |
setRNG Set (Normal) Random Number Generator and Seed | 2022.4-1 | 2022.4-1 |
sets Sets, Generalized Sets, Customizable Sets and Intervals | 1.0-21 | 1.0-21 |
settings Software Option Settings Manager for R | 0.2.7 | 0.2.7 |
Seurat Tools for Single Cell Genomics | 4.3.0 | 4.3.0 |
SeuratObject Data Structures for Single Cell Data | 5.0.1 | 5.0.1 |
sf Simple Features for R | 1.0-15 | 1.0-15 |
sfa Stochastic Frontier Analysis | 1.0-1 | 1.0-1 |
sfd Space-Filling Design Library | 0.1.0 | 0.1.0 |
sFFLHD Sequential Full Factorial-Based Latin Hypercube Design | 0.1.2 | 0.1.2 |
sfheaders Converts Between R Objects and Simple Feature Objects | 0.4.4 | 0.4.4 |
sfsmisc Utilities from 'Seminar fuer Statistik' ETH Zurich | 1.1-16 | 1.1-16 |
sftime Classes and Methods for Simple Feature Objects that Have a Time Column | 0.2-0 | 0.2-0 |
sftrack Modern Classes for Tracking and Movement Data | 0.5.4 | 0.5.4 |
sgd Stochastic Gradient Descent for Scalable Estimation | 1.1.1 | 1.1.1 |
sgeostat An Object-Oriented Framework for Geostatistical Modeling in S+ | 1.0-27 | 1.0-27 |
SGL Fit a GLM (or Cox Model) with a Combination of Lasso and Group Lasso Regularization | 1.3 | 1.3 |
sglg Fitting Semi-Parametric Generalized log-Gamma Regression Models | 0.2.2 | 0.2.2 |
sgmodel Solves a Generic Stochastic Growth Model with a Representative Agent | 0.1.1 | 0.1.1 |
sgt Skewed Generalized T Distribution Tree | 2.0 | 2.0 |
shades Simple Colour Manipulation | 1.4.0 | 1.4.0 |
shadowtext Shadow Text Grob and Layer | 0.1.4 | 0.1.4 |
shape Functions for Plotting Graphical Shapes, Colors | 1.4.6 | 1.4.6 |
shapefiles Read and Write ESRI Shapefiles | 0.7.2 | 0.7.2 |
shapes Statistical Shape Analysis | 1.2.7 | 1.2.7 |
SharpeR Statistical Significance of the Sharpe Ratio | 1.3.0 | 1.3.0 |
shiny Web Application Framework for R | 1.8.0 | 1.8.0 |
shinyAce Ace Editor Bindings for Shiny | 0.4.2 | 0.4.2 |
shinybrms Graphical User Interface ('shiny' App) for 'brms' | 1.7.0 | 1.7.0 |
shinyBS Twitter Bootstrap Components for Shiny | 0.61.1 | 0.61.1 |
shinycssloaders Add Loading Animations to a 'shiny' Output While It's Recalculating | 1.0.0 | 1.0.0 |
shinydashboard Create Dashboards with 'Shiny' | 0.7.2 | 0.7.2 |
shinydashboardPlus Add More 'AdminLTE2' Components to 'shinydashboard' | 2.0.3 | 2.0.3 |
shinyFiles A Server-Side File System Viewer for Shiny | 0.9.3 | 0.9.3 |
ShinyItemAnalysis Test and Item Analysis via Shiny | 1.5.0 | 1.5.0 |
shinyjqui 'jQuery UI' Interactions and Effects for Shiny | 0.4.1 | 0.4.1 |
shinyjs Easily Improve the User Experience of Your Shiny Apps in Seconds | 2.1.0 | 2.1.0 |
shinystan Interactive Visual and Numerical Diagnostics and Posterior Analysis for Bayesian Models | 2.6.0 | 2.6.0 |
shinythemes Themes for Shiny | 1.2.0 | 1.2.0 |
shinyTree jsTree Bindings for Shiny | 0.3.1 | 0.3.1 |
shinyWidgets Custom Inputs Widgets for Shiny | 0.8.1 | 0.8.1 |
showimage Show an Image on an 'R' Graphics Device | 1.0.0 | 1.0.0 |
showtext Using Fonts More Easily in R Graphs | 0.9-6 | 0.9-6 |
showtextdb Font Files for the 'showtext' Package | 3.0 | 3.0 |
sievePH Sieve Analysis Methods for Proportional Hazards Models | 1.0.4 | 1.0.4 |
sigclust Statistical Significance of Clustering | 1.1.0.1 | 1.1.0.1 |
sigmoid Sigmoid Functions for Machine Learning | 1.4.0 | 1.4.0 |
Signac Analysis of Single-Cell Chromatin Data | 1.14.0 | 1.14.0 |
signal Signal Processing | 1.8-0 | 1.8-0 |
Sim.DiffProc Simulation of Diffusion Processes | 4.8 | 4.8 |
simcdm Simulate Cognitive Diagnostic Model ('CDM') Data | 0.1.2 | 0.1.2 |
SimCop Simulate from Arbitrary Copulae | 0.7.0 | 0.7.0 |
SimDesign Structure for Organizing Monte Carlo Simulation Designs | 2.17.1 | 2.17.1 |
simecol Simulation of Ecological (and Other) Dynamic Systems | 0.8-14 | 0.8-14 |
simex SIMEX- And MCSIMEX-Algorithm for Measurement Error Models | 1.8 | 1.8 |
simexaft simexaft | 1.0.7.1 | 1.0.7.1 |
simfinapi Accessing 'SimFin' Data | 0.2.4 | 0.2.4 |
simFrame Simulation Framework | 0.5.4 | 0.5.4 |
simglm Simulate Models Based on the Generalized Linear Model | 0.8.9 | 0.8.9 |
SimHaz Simulated Survival and Hazard Analysis for Time-Dependent Exposure | 0.1 | 0.1 |
SimilarityMeasures Trajectory Similarity Measures | 1.4 | 1.4 |
SimInf A Framework for Data-Driven Stochastic Disease Spread Simulations | 9.6.0 | 9.6.0 |
SimJoint Simulate Joint Distribution | 0.3.12 | 0.3.12 |
simml Single-Index Models with Multiple-Links | 0.3.0 | 0.3.0 |
simMSM Simulation of Event Histories for Multi-State Models | 1.1.42 | 1.1.42 |
simPH Simulate and Plot Estimates from Cox Proportional Hazards Models | 1.3.13 | 1.3.13 |
simpleboot Simple Bootstrap Routines | 1.1-7 | 1.1-7 |
simplermarkdown Simple Engine for Generating Reports using R | 0.0.4 | 0.0.4 |
SimplicialCubature Integration of Functions Over Simplices | 1.3 | 1.3 |
simPop Simulation of Complex Synthetic Data Information | 2.1.3 | 2.1.3 |
simputation Simple Imputation | 0.2.8 | 0.2.8 |
simrel Simulation of Multivariate Linear Model Data | 2.1.0 | 2.1.0 |
SiMRiv Simulating Multistate Movements in River/Heterogeneous Landscapes | 1.0.6 | 1.0.6 |
SimSCRPiecewise 'Simulates Univariate and Semi-Competing Risks Data Given Covariates and Piecewise Exponential Baseline Hazards' | 0.1.1 | 0.1.1 |
simsem SIMulated Structural Equation Modeling | 0.5-16 | 0.5-16 |
simsl Single-Index Models with a Surface-Link | 0.2.1 | 0.2.1 |
simsurv Simulate Survival Data | 1.0.0 | 1.0.0 |
SimSurvey Test Surveys by Simulating Spatially-Correlated Populations | 0.1.6 | 0.1.6 |
SimSurvNMarker Simulate Survival Time and Markers | 0.1.3 | 0.1.3 |
SIN A SINful Approach to Selection of Gaussian Graphical Markov Models | 0.6 | 0.6 |
SingleCaseES A Calculator for Single-Case Effect Sizes | 0.7.2 | 0.7.2 |
SingleCellExperiment | 1.20.0 | 1.20.0 |
siplab Spatial Individual-Plant Modelling | 1.6 | 1.6 |
sirt Supplementary Item Response Theory Models | 4.0-32 | 4.0-32 |
SIS Sure Independence Screening | 0.8-8 | 0.8-8 |
sitmo Parallel Pseudo Random Number Generator (PPRNG) 'sitmo' Header Files | 2.0.2 | 2.0.2 |
sjlabelled Labelled Data Utility Functions | 1.2.0 | 1.2.0 |
sjmisc Data and Variable Transformation Functions | 2.8.10 | 2.8.10 |
sjPlot Data Visualization for Statistics in Social Science | 2.8.15 | 2.8.15 |
sjstats Collection of Convenient Functions for Common Statistical Computations | 0.18.2 | 0.18.2 |
skellam Densities and Sampling for the Skellam Distribution | 0.2.0 | 0.2.0 |
SkewHyperbolic The Skew Hyperbolic Student t-Distribution | 0.4-2 | 0.4-2 |
skewlmm Scale Mixture of Skew-Normal Linear Mixed Models | 1.1.0 | 1.1.0 |
skewt The Skewed Student-t Distribution | 1.0 | 1.0 |
skmeans Spherical k-Means Clustering | 0.2-16 | 0.2-16 |
skynet Generates Networks from BTS Data | 1.4.3 | 1.4.3 |
slackr Send Messages, Images, R Objects and Files to 'Slack' Channels/Users | 3.3.1 | 3.3.1 |
slam Sparse Lightweight Arrays and Matrices | 0.1-50 | 0.1-50 |
SLBDD Statistical Learning for Big Dependent Data | 0.0.4 | 0.0.4 |
sld Estimation and Use of the Quantile-Based Skew Logistic Distribution | 1.0.1 | 1.0.1 |
sleekts 4253H, Twice Smoothing | 1.0.2 | 1.0.2 |
Sleuth2 Data Sets from Ramsey and Schafer's "Statistical Sleuth (2nd Ed)" | 2.0-7 | 2.0-7 |
Sleuth3 Data Sets from Ramsey and Schafer's "Statistical Sleuth (3rd Ed)" | 1.0-6 | 1.0-6 |
SLHD Maximin-Distance (Sliced) Latin Hypercube Designs | 2.1-1 | 2.1-1 |
slider Sliding Window Functions | 0.3.1 | 0.3.1 |
slippymath Slippy Map Tile Tools | 0.3.1 | 0.3.1 |
sm Smoothing Methods for Nonparametric Regression and Density Estimation | 2.2-5.7.1 | 2.2-5.7.1 |
smacof Multidimensional Scaling | 2.1-5 | 2.1-5 |
smacofx Flexible Multidimensional Scaling and 'smacof' Extensions | 1.6-1 | 1.6-1 |
smacpod Statistical Methods for the Analysis of Case-Control Point Data | 2.6 | 2.6 |
SmallCountRounding Small Count Rounding of Tabular Data | 1.0.3 | 1.0.3 |
smam Statistical Modeling of Animal Movements | 0.7.2 | 0.7.2 |
smartsizer Power Analysis for a SMART Design | 1.0.3 | 1.0.3 |
smcfcs Multiple Imputation of Covariates by Substantive Model Compatible Fully Conditional Specification | 1.7.1 | 1.7.1 |
smcure Fit Semiparametric Mixture Cure Models | 2.1 | 2.1 |
smd Compute Standardized Mean Differences | 0.6.6 | 0.6.6 |
smerc Statistical Methods for Regional Counts | 1.8.3 | 1.8.3 |
SmithWilsonYieldCurve Smith-Wilson Yield Curve Construction | 1.0.1 | 1.0.1 |
smoof Single and Multi-Objective Optimization Test Functions | 1.6.0.3 | 1.6.0.3 |
smooth Forecasting Using State Space Models | 4.0.0 | 4.0.0 |
smoother Functions Relating to the Smoothing of Numerical Data | 1.1 | 1.1 |
SmoothHazard Estimation of Smooth Hazard Models for Interval-Censored Data | 2023.06.27 | 2023.06.27 |
smoothHR Smooth Hazard Ratio Curves Taking a Reference Value | 1.0.4 | 1.0.4 |
smoothmest Smoothed M-Estimators for 1-Dimensional Location | 0.1-3 | 0.1-3 |
smoothSurv Survival Regression with Smoothed Error Distribution | 2.5 | 2.5 |
smoots Nonparametric Estimation of the Trend and Its Derivatives in TS | 1.1.4 | 1.1.4 |
smovie Some Movies to Illustrate Concepts in Statistics | 1.1.6 | 1.1.6 |
SMPracticals Practicals for Use with Davison (2003) Statistical Models | 1.4-3 | 1.4-3 |
SMR Externally Studentized Midrange Distribution | 2.1.0 | 2.1.0 |
sms Spatial Microsimulation | 2.3.1 | 2.3.1 |
SMVar Structural Model for Variances | 1.3.4 | 1.3.4 |
sn The Skew-Normal and Related Distributions Such as the Skew-t and the SUN | 2.1.1 | 2.1.1 |
sna Tools for Social Network Analysis | 2.7-2 | 2.7-2 |
snakecase Convert Strings into any Case | 0.11.1 | 0.11.1 |
snapshot Gadget N-body cosmological simulation code snapshot I/O utilities | 0.1.2 | 0.1.2 |
snow Simple Network of Workstations | 0.4-4 | 0.4-4 |
SnowballC Snowball Stemmers Based on the C 'libstemmer' UTF-8 Library | 0.7.1 | 0.7.1 |
snowfall Easier Cluster Computing (Based on 'snow') | 1.84-6.3 | 1.84-6.3 |
snowFT Fault Tolerant Simple Network of Workstations | 1.6-1 | 1.6-1 |
SOAs Creation of Stratum Orthogonal Arrays | 1.4 | 1.4 |
soc.ca Specific Correspondence Analysis for the Social Sciences | 0.8.0 | 0.8.0 |
socceR Evaluating Sport Tournament Predictions | 0.1.1 | 0.1.1 |
socialmixr Social Mixing Matrices for Infectious Disease Modelling | 0.3.1 | 0.3.1 |
sodium A Modern and Easy-to-Use Crypto Library | 1.3.1 | 1.3.1 |
softImpute Matrix Completion via Iterative Soft-Thresholded SVD | 1.4-1 | 1.4-1 |
soiltexture Functions for Soil Texture Plot, Classification and Transformation | 1.5.1 | 1.5.1 |
soilwater Implementation of Parametric Formulas for Soil Water Retention or Conductivity Curve | 1.0.5 | 1.0.5 |
solaR Radiation and Photovoltaic Systems | 0.46 | 0.46 |
solartime Utilities Dealing with Solar Time Such as Sun Position and Time of Sunrise | 0.0.2 | 0.0.2 |
solrium General Purpose R Interface to 'Solr' | 1.2.0 | 1.2.0 |
SolveSAPHE Solver Suite for Alkalinity-PH Equations | 2.1.0 | 2.1.0 |
som Self-Organizing Map | 0.3-5.1 | 0.3-5.1 |
soma General-Purpose Optimisation with the Self-Organising Migrating Algorithm | 1.2.0 | 1.2.0 |
soptdmaeA Sequential Optimal Designs for Two-Colour cDNA Microarray Experiments | 1.0.0 | 1.0.0 |
SortedEffects Estimation and Inference Methods for Sorted Causal Effects and Classification Analysis | 1.7.0 | 1.7.0 |
sorvi Functions for Finnish Open Data | 0.8.21 | 0.8.21 |
sourcetools Tools for Reading, Tokenizing and Parsing R Code | 0.1.7-1 | 0.1.7-1 |
sp Classes and Methods for Spatial Data | 1.6-0 | 1.6-0 |
sp23design Design and Simulation of Seamless Phase II-III Clinical Trials | 0.9-1 | 0.9-1 |
spacefillr Space-Filling Random and Quasi-Random Sequences | 0.3.2 | 0.3.2 |
spacetime Classes and Methods for Spatio-Temporal Data | 1.3-1 | 1.3-1 |
SPADAR Spherical Projections of Astronomical Data | 1.0 | 1.0 |
SpaDES.tools Additional Tools for Developing Spatially Explicit Discrete Event Simulation (SpaDES) Models | 1.0.0 | 1.0.0 |
spam SPArse Matrix | 2.10-0 | 2.10-0 |
spam64 64-Bit Extension of the SPArse Matrix R Package 'spam' | 2.10-0 | 2.10-0 |
spaMM Mixed-Effect Models, with or without Spatial Random Effects | 4.4.16 | 4.4.16 |
spanel Spatial Panel Data Models | 0.1 | 0.1 |
spant MR Spectroscopy Analysis Tools | 2.17.0 | 2.17.0 |
sparkline 'jQuery' Sparkline 'htmlwidget' | 2.0 | 2.0 |
sparklyr R Interface to Apache Spark | 1.8.4 | 1.8.4 |
sparktex Generate LaTeX sparklines in R | 0.1 | 0.1 |
SPARQL SPARQL client | 1.16 | 1.16 |
sparr Spatial and Spatiotemporal Relative Risk | 2.3-10 | 2.3-10 |
SparseArray | 1.4.8 | 1.4.8 |
sparsediscrim Sparse and Regularized Discriminant Analysis | 0.3.0 | 0.3.0 |
SparseFactorAnalysis Scaling Count and Binary Data with Sparse Factor Analysis | 1.0 | 1.0 |
sparseFLMM Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data | 0.4.1 | 0.4.1 |
SparseGrid Sparse grid integration in R | 0.8.2 | 0.8.2 |
sparseinv Computation of the Sparse Inverse Subset | 0.1.3 | 0.1.3 |
sparseLDA Sparse Discriminant Analysis | 0.1-9 | 0.1-9 |
SparseM Sparse Linear Algebra | 1.81 | 1.81 |
sparseMatrixStats | 1.12.2 | 1.12.2 |
sparseMVN Multivariate Normal Functions for Sparse Covariance and Precision Matrices | 0.2.2 | 0.2.2 |
sparsesvd Sparse Truncated Singular Value Decomposition (from 'SVDLIBC') | 0.2-2 | 0.2-2 |
sparsevar Sparse VAR/VECM Models Estimation | 0.1.0 | 0.1.0 |
spate Spatio-Temporal Modeling of Large Data Using a Spectral SPDE Approach | 1.7.5 | 1.7.5 |
SPAtest Score Test and Meta-Analysis Based on Saddlepoint Approximation | 3.1.2 | 3.1.2 |
spatgraphs Graph Edge Computations for Spatial Point Patterns | 3.4 | 3.4 |
spatial Functions for Kriging and Point Pattern Analysis | 7.3-17 | 7.3-17 |
spatialCovariance Computation of Spatial Covariance Matrices for Data on Rectangles | 0.6-9 | 0.6-9 |
SpatialEpi Methods and Data for Spatial Epidemiology | 1.2.8 | 1.2.8 |
SpatialExtremes Modelling Spatial Extremes | 2.1-0 | 2.1-0 |
SpatialPosition Spatial Position Models | 2.1.2 | 2.1.2 |
spatialprobit Spatial Probit Models | 1.0.1 | 1.0.1 |
spatialreg Spatial Regression Analysis | 1.3-1 | 1.3-1 |
spatialsample Spatial Resampling Infrastructure | 0.5.1 | 0.5.1 |
SpatialTools Tools for Spatial Data Analysis | 1.0.5 | 1.0.5 |
spatialwidget Formats Spatial Data for Use in Htmlwidgets | 0.2.5 | 0.2.5 |
spatsoc Group Animal Relocation Data by Spatial and Temporal Relationship | 0.2.2 | 0.2.2 |
spatstat Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests | 3.0-6 | 3.0-6 |
spatstat.data Datasets for 'spatstat' Family | 3.0-4 | 3.0-4 |
spatstat.explore Exploratory Data Analysis for the 'spatstat' Family | 3.2-3 | 3.2-3 |
spatstat.geom Geometrical Functionality of the 'spatstat' Family | 3.2-8 | 3.2-8 |
spatstat.linnet Linear Networks Functionality of the 'spatstat' Family | 3.1-1 | 3.1-1 |
spatstat.model Parametric Statistical Modelling and Inference for the 'spatstat' Family | 3.2-6 | 3.2-6 |
spatstat.random Random Generation Functionality for the 'spatstat' Family | 3.2-2 | 3.2-2 |
spatstat.sparse Sparse Three-Dimensional Arrays and Linear Algebra Utilities | 3.0-3 | 3.0-3 |
spatstat.univar One-Dimensional Probability Distribution Support for the 'spatstat' Family | 3.0-1 | 3.0-1 |
spatstat.utils Utility Functions for 'spatstat' | 3.0-4 | 3.0-4 |
spatsurv Bayesian Spatial Survival Analysis with Parametric Proportional Hazards Models | 1.8 | 1.8 |
spBayes Univariate and Multivariate Spatial-Temporal Modeling | 0.4-7 | 0.4-7 |
spBayesSurv Bayesian Modeling and Analysis of Spatially Correlated Survival Data | 1.1.7 | 1.1.7 |
Spbsampling Spatially Balanced Sampling | 1.3.5 | 1.3.5 |
spc Statistical Process Control -- Calculation of ARL and Other Control Chart Performance Measures | 0.6.6 | 0.6.6 |
spCP Spatially Varying Change Points | 1.2 | 1.2 |
spd Semi Parametric Distribution | 2.0-1 | 2.0-1 |
spData Datasets for Spatial Analysis | 2.3.0 | 2.3.0 |
spdep Spatial Dependence: Weighting Schemes, Statistics | 1.3-1 | 1.3-1 |
spectral Common Methods of Spectral Data Analysis | 2.0 | 2.0 |
spectralAnalysis Pre-Process, Visualize and Analyse Spectral Data | 4.3.3 | 4.3.3 |
spectralGraphTopology Learning Graphs from Data via Spectral Constraints | 0.2.3 | 0.2.3 |
Spectrum Fast Adaptive Spectral Clustering for Single and Multi-View Data | 1.1 | 1.1 |
spef Semiparametric Estimating Functions | 1.0.9 | 1.0.9 |
speff2trial Semiparametric Efficient Estimation for a Two-Sample Treatment Effect | 1.0.5 | 1.0.5 |
SPEI Calculation of the Standardized Precipitation-Evapotranspiration Index | 1.8.1 | 1.8.1 |
spelling Tools for Spell Checking in R | 2.2.1 | 2.2.1 |
sperrorest Perform Spatial Error Estimation and Variable Importance Assessment | 3.0.5 | 3.0.5 |
spgwr Geographically Weighted Regression | 0.6-35 | 0.6-35 |
SphericalCubature Numerical Integration over Spheres and Balls in n-Dimensions; Multivariate Polar Coordinates | 1.5 | 1.5 |
sphet Estimation of Spatial Autoregressive Models with and without Heteroskedastic Innovations | 2.0 | 2.0 |
spiderbar Parse and Test Robots Exclusion Protocol Files and Rules | 0.2.5 | 0.2.5 |
spikeslab Prediction and Variable Selection Using Spike and Slab Regression | 1.1.6 | 1.1.6 |
spikeSlabGAM Bayesian Variable Selection and Model Choice for Generalized Additive Mixed Models | 1.1-19 | 1.1-19 |
spind Spatial Methods and Indices | 2.2.1 | 2.2.1 |
splancs Spatial and Space-Time Point Pattern Analysis | 2.01-44 | 2.01-44 |
4.4.1 | 4.4.1 | |
splines2 Regression Spline Functions and Classes | 0.5.1 | 0.5.1 |
splinetree Longitudinal Regression Trees and Forests | 0.2.0 | 0.2.0 |
splitstackshape Stack and Reshape Datasets After Splitting Concatenated Values | 1.4.8 | 1.4.8 |
splitTools Tools for Data Splitting | 1.0.1 | 1.0.1 |
splm Econometric Models for Spatial Panel Data | 1.6-5 | 1.6-5 |
spls Sparse Partial Least Squares (SPLS) Regression and Classification | 2.2-3 | 2.2-3 |
splus2R Supplemental S-PLUS Functionality in R | 1.3-4 | 1.3-4 |
splusTimeDate Times and Dates from 'S-PLUS' | 2.5.4 | 2.5.4 |
splusTimeSeries Time Series from 'S-PLUS' | 1.5.5 | 1.5.5 |
spmoran Fast Spatial and Spatio-Temporal Regression using Moran Eigenvectors | 0.2.2.9 | 0.2.2.9 |
SPORTSCausal Spillover Time Series Causal Inference | 1.0 | 1.0 |
SportsTour Display Tournament Fixtures using Knock Out and Round Robin Techniques | 0.1.0 | 0.1.0 |
sportyR Plot Scaled 'ggplot' Representations of Sports Playing Surfaces | 2.2.1 | 2.2.1 |
SPOT Sequential Parameter Optimization Toolbox | 2.11.14 | 2.11.14 |
SpotSampling SPatial and Optimally Temporal (SPOT) Sampling | 0.1.0 | 0.1.0 |
spray Sparse Arrays and Multivariate Polynomials | 1.0-26 | 1.0-26 |
spselect Selecting Spatial Scale of Covariates in Regression Models | 0.0.1 | 0.0.1 |
spsur Spatial Seemingly Unrelated Regression Models | 1.0.2.5 | 1.0.2.5 |
spsurvey Spatial Sampling Design and Analysis | 5.5.1 | 5.5.1 |
spTimer Spatio-Temporal Bayesian Modelling | 3.3.2 | 3.3.2 |
sqldf Manipulate R Data Frames Using SQL | 0.4-11 | 0.4-11 |
SQUAREM Squared Extrapolation Methods for Accelerating EM-Like Monotone Algorithms | 2021.1 | 2021.1 |
squash Color-Based Plots for Multivariate Visualization | 1.0.9 | 1.0.9 |
squashinformr Politely Web Scrape Data from SquashInfo | 0.2.6 | 0.2.6 |
srvyr 'dplyr'-Like Syntax for Summary Statistics of Survey Data | 1.2.0 | 1.2.0 |
ssanv Sample Size Adjusted for Nonadherence or Variability of Input Parameters | 1.1 | 1.1 |
SSBtools Statistics Norway's Miscellaneous Tools | 1.5.0 | 1.5.0 |
ssfa Spatial Stochastic Frontier Analysis | 1.2.2 | 1.2.2 |
ssgraph Bayesian Graph Structure Learning using Spike-and-Slab Priors | 1.14 | 1.14 |
ssh Secure Shell (SSH) Client for R | 0.9.1 | 0.9.1 |
ssize.fdr Sample Size Calculations for Microarray Experiments | 1.3 | 1.3 |
ssMousetrack Bayesian State-Space Modeling of Mouse-Tracking Experiments via Stan | 1.1.6 | 1.1.6 |
ssmrob Robust Estimation and Inference in Sample Selection Models | 1.0 | 1.0 |
SSN Spatial Modeling on Stream Networks | 1.1.17 | 1.1.17 |
SSRMST Sample Size Calculation using Restricted Mean Survival Time | 0.1.1 | 0.1.1 |
stable Probability Functions and Generalized Regression Models for Stable Distributions | 1.1.6 | 1.1.6 |
stabledist Stable Distribution Functions | 0.7-1 | 0.7-1 |
stablelearner Stability Assessment of Statistical Learning Methods | 0.1-3 | 0.1-3 |
stabs Stability Selection with Error Control | 0.6-4 | 0.6-4 |
StAMPP Statistical Analysis of Mixed Ploidy Populations | 1.6.3 | 1.6.3 |
stampr Spatial Temporal Analysis of Moving Polygons | 0.3.1 | 0.3.1 |
StanHeaders C++ Header Files for Stan | 2.32.10 | 2.32.10 |
STAR Spike Train Analysis with R | 0.3-7 | 0.3-7 |
stargazer Well-Formatted Regression and Summary Statistics Tables | 5.2.3 | 5.2.3 |
starma Modelling Space Time AutoRegressive Moving Average (STARMA) Processes | 1.3 | 1.3 |
stars Spatiotemporal Arrays, Raster and Vector Data Cubes | 0.6-4 | 0.6-4 |
STARTS Functions for the STARTS Model | 1.3-8 | 1.3-8 |
startupmsg Utilities for Start-Up Messages | 0.9.6 | 0.9.6 |
statcanR Client for Statistics Canada's Open Economic Data | 0.2.6 | 0.2.6 |
statcheck Extract Statistics from Articles and Recompute P-Values | 1.3.0 | 1.3.0 |
statebins Create United States Uniform Cartogram Heatmaps | 1.4.0 | 1.4.0 |
statespacer State Space Modelling in 'R' | 0.5.0 | 0.5.0 |
stationaRy Detailed Meteorological Data from Stations All Over the World | 0.5.1 | 0.5.1 |
statip Statistical Functions for Probability Distributions and Regression | 0.2.3 | 0.2.3 |
StatMatch Statistical Matching or Data Fusion | 1.4.1 | 1.4.1 |
statmod Statistical Modeling | 1.4.37 | 1.4.37 |
statnet.common Common R Scripts and Utilities Used by the Statnet Project Software | 4.10.0 | 4.10.0 |
StatRank Statistical Rank Aggregation: Inference, Evaluation, and Visualization | 0.0.6 | 0.0.6 |
stats | 4.4.1 | 4.4.1 |
stats4 | 4.4.1 | 4.4.1 |
stdReg Regression Standardization | 3.4.1 | 3.4.1 |
steadyICA ICA and Tests of Independence via Multivariate Distance Covariance | 1.0 | 1.0 |
SteinIV Semi-Parametric Stein-Like Estimator with Instrumental Variables | 0.1-1 | 0.1-1 |
stellaR Evolutionary Tracks and Isochrones from Pisa Stellar Evolution Database | 0.3-4 | 0.3-4 |
Stem Spatio-temporal models in R | 1.0 | 1.0 |
StempCens Spatio-Temporal Estimation and Prediction for Censored/Missing Responses | 1.1.0 | 1.1.0 |
stepp Subpopulation Treatment Effect Pattern Plot (STEPP) | 3.2.6 | 3.2.6 |
stepPlr L2 Penalized Logistic Regression with Stepwise Variable Selection | 0.93 | 0.93 |
STFTS Statistical Tests for Functional Time Series | 0.1.0 | 0.1.0 |
stinepack Stineman, a Consistently Well Behaved Method of Interpolation | 1.4 | 1.4 |
stlplus Enhanced Seasonal Decomposition of Time Series by Loess | 0.5.1 | 0.5.1 |
stm Estimation of the Structural Topic Model | 1.3.7 | 1.3.7 |
STMedianPolish Spatio-Temporal Median Polish | 0.2 | 0.2 |
stochQN Stochastic Limited Memory Quasi-Newton Optimizers | 0.1.2-1 | 0.1.2-1 |
stochvol Efficient Bayesian Inference for Stochastic Volatility (SV) Models | 3.2.3 | 3.2.3 |
stockfish Analyze Chess Games with the 'Stockfish' Engine | 1.0.0 | 1.0.0 |
stopwords Multilingual Stopword Lists | 2.3 | 2.3 |
storr Simple Key Value Stores | 1.2.5 | 1.2.5 |
stplanr Sustainable Transport Planning | 1.0.0 | 1.0.0 |
stR Seasonal Trend Decomposition Using Regression | 0.6 | 0.6 |
strand A Framework for Investment Strategy Simulation | 0.2.0 | 0.2.0 |
stratamatch Stratification and Matching for Large Observational Data Sets | 0.1.9 | 0.1.9 |
stratification Univariate Stratification of Survey Populations | 2.2-7 | 2.2-7 |
StratifiedRF Builds Trees by Sampling Variables in Groups | 0.2.2 | 0.2.2 |
streamDepletr Estimate Streamflow Depletion Due to Groundwater Pumping | 0.2.0 | 0.2.0 |
StreamMetabolism Calculate Single Station Metabolism from Diurnal Oxygen Curves | 1.1.3 | 1.1.3 |
streamR Access to Twitter Streaming API via R | 0.4.5 | 0.4.5 |
stringdist Approximate String Matching, Fuzzy Text Search, and String Distance Functions | 0.9.12 | 0.9.12 |
stringfish Alt String Implementation | 0.16.0 | 0.16.0 |
stringi Fast and Portable Character String Processing Facilities | 1.8.3 | 1.8.3 |
stringmagic Character String Operations and Interpolation, Magic Edition | 1.0.0 | 1.0.0 |
stringr Simple, Consistent Wrappers for Common String Operations | 1.5.1 | 1.5.1 |
strucchange Testing, Monitoring, and Dating Structural Changes | 1.5-3 | 1.5-3 |
strucchangeRcpp Testing, Monitoring, and Dating Structural Changes: C++ Version | 1.5-3-1.0.4 | 1.5-3-1.0.4 |
stsm Structural Time Series Models | 1.9 | 1.9 |
styler Non-Invasive Pretty Printing of R Code | 1.10.3 | 1.10.3 |
subdetect Detect Subgroup with an Enhanced Treatment Effect | 1.2 | 1.2 |
subplex Unconstrained Optimization using the Subplex Algorithm | 1.8 | 1.8 |
subscore Computing Subscores in Classical Test Theory and Item Response Theory | 3.3 | 3.3 |
subselect Selecting Variable Subsets | 0.15.5 | 0.15.5 |
sugrrants Supporting Graphs for Analysing Time Series | 0.2.8 | 0.2.8 |
SummarizedExperiment | 1.28.0 | 1.28.0 |
summclust Module to Compute Influence and Leverage Statistics for Regression Models with Clustered Errors | 0.7.2 | 0.7.2 |
suncalc Compute Sun Position, Sunlight Phases, Moon Position and Lunar Phase | 0.5.0 | 0.5.0 |
sundialr An Interface to 'SUNDIALS' Ordinary Differential Equation (ODE) Solvers | 0.1.4.1 | 0.1.4.1 |
suntools Calculate Sun Position, Sunrise, Sunset, Solar Noon and Twilight | 1.0.0 | 1.0.0 |
SuperLearner Super Learner Prediction | 2.0-28.1 | 2.0-28.1 |
superpc Supervised Principal Components | 1.12 | 1.12 |
SuppDists Supplementary Distributions | 1.1-9.7 | 1.1-9.7 |
support.CEs Basic Functions for Supporting an Implementation of Choice Experiments | 0.7-0 | 0.7-0 |
suRtex LaTeX descriptive statistic reporting for survey data | 0.9 | 0.9 |
surv2sampleComp Inference for Model-Free Between-Group Parameters for Censored Survival Data | 1.0-5 | 1.0-5 |
survAUC Estimators of Prediction Accuracy for Time-to-Event Data | 1.2-0 | 1.2-0 |
survC1 C-Statistics for Risk Prediction Models with Censored Survival Data | 1.0-3 | 1.0-3 |
SurvCorr Correlation of Bivariate Survival Times | 1.1 | 1.1 |
surveillance Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena | 1.22.1 | 1.22.1 |
survexp.fr Relative Survival, AER and SMR Based on French Death Rates | 1.1 | 1.1 |
survey Analysis of Complex Survey Samples | 4.2-1 | 4.2-1 |
surveybootstrap Bootstrap with Survey Data | 0.0.3 | 0.0.3 |
surveydata Tools to Work with Survey Data | 0.2.7 | 0.2.7 |
surveyoutliers Helps Manage Outliers in Sample Surveys | 0.1 | 0.1 |
surveyplanning Survey Planning Tools | 4.0 | 4.0 |
surveysd Survey Standard Error Estimation for Cumulated Estimates and their Differences in Complex Panel Designs | 1.3.1 | 1.3.1 |
survIDINRI IDI and NRI for Comparing Competing Risk Prediction Models with Censored Survival Data | 1.1-2 | 1.1-2 |
survival Survival Analysis | 3.7-0 | 3.7-0 |
survivalMPL Penalised Maximum Likelihood for Survival Analysis Models | 0.2-3 | 0.2-3 |
survivalROC Time-Dependent ROC Curve Estimation from Censored Survival Data | 1.0.3.1 | 1.0.3.1 |
survJamda.data Data for Package 'survJambda' | 1.0.2 | 1.0.2 |
SurvLong Analysis of Proportional Hazards Model with Sparse Longitudinal Covariates | 1.4 | 1.4 |
survminer Drawing Survival Curves using 'ggplot2' | 0.4.9 | 0.4.9 |
survMisc Miscellaneous Functions for Survival Data | 0.5.6 | 0.5.6 |
survPresmooth Presmoothed Estimation in Survival Analysis | 1.1-11 | 1.1-11 |
SurvRegCensCov Weibull Regression for a Right-Censored Endpoint with Interval-Censored Covariate | 1.7 | 1.7 |
survRM2 Comparing Restricted Mean Survival Time | 1.0-3 | 1.0-3 |
survsim Simulation of Simple and Complex Survival Data | 1.1.8 | 1.1.8 |
survSNP Power Calculations for SNP Studies with Censored Outcomes | 0.26 | 0.26 |
susieR Sum of Single Effects Linear Regression | 0.11.92 | 0.11.92 |
sva | 3.46.0 | 3.46.0 |
svars Data-Driven Identification of SVAR Models | 1.3.11 | 1.3.11 |
svd Interfaces to Various State-of-Art SVD and Eigensolvers | 0.5.5 | 0.5.5 |
svglite An 'SVG' Graphics Device | 2.1.3 | 2.1.3 |
svgPanZoom R 'Htmlwidget' to Add Pan and Zoom to Almost any R Graphic | 0.3.4 | 0.3.4 |
svMisc 'SciViews' - Miscellaneous Functions | 1.2.3 | 1.2.3 |
svmpath The SVM Path Algorithm | 0.970 | 0.970 |
svrep Tools for Creating, Updating, and Analyzing Survey Replicate Weights | 0.6.3 | 0.6.3 |
svUnit 'SciViews' - Unit, Integration and System Testing | 1.0.6 | 1.0.6 |
SvyNom Nomograms for Right-Censored Outcomes from Survey Designs | 1.2 | 1.2 |
swagger Dynamically Generates Documentation from a 'Swagger' Compliant API | 3.33.1 | 3.33.1 |
SwarmSVM Ensemble Learning Algorithms Based on Support Vector Machines | 0.1-7 | 0.1-7 |
sweep Tidy Tools for Forecasting | 0.2.3 | 0.2.3 |
swgee Simulation Extrapolation Inverse Probability Weighted Generalized Estimating Equations | 1.4 | 1.4 |
SwimmeR Data Import, Cleaning, and Conversions for Swimming Results | 0.14.2 | 0.14.2 |
swirl Learn R, in R | 2.4.5 | 2.4.5 |
swirlify A Toolbox for Writing 'swirl' Courses | 0.5.3 | 0.5.3 |
swmmr R Interface for US EPA's SWMM | 0.9.1 | 0.9.1 |
sylly Hyphenation and Syllable Counting for Text Analysis | 0.1-6 | 0.1-6 |
sym.arma Autoregressive and Moving Average Symmetric Models | 1.0 | 1.0 |
symbolicQspray Multivariate Polynomials with Symbolic Parameters in their Coefficients | 1.1.0 | 1.1.0 |
symengine Interface to the 'SymEngine' Library | 0.2.4 | 0.2.4 |
symmoments Symbolic Central and Noncentral Moments of the Multivariate Normal Distribution | 1.2.1 | 1.2.1 |
SymTS Symmetric Tempered Stable Distributions | 1.0-2 | 1.0-2 |
Synth Synthetic Control Group Method for Comparative Case Studies | 1.1-8 | 1.1-8 |
synthACS Synthetic Microdata and Spatial MicroSimulation Modeling for ACS Data | 1.7.1 | 1.7.1 |
synthesis Generate Synthetic Data from Statistical Models | 1.2.4 | 1.2.4 |
synthpop Generating Synthetic Versions of Sensitive Microdata for Statistical Disclosure Control | 1.8-0 | 1.8-0 |
SynthTools Tools and Tests for Experiments with Partially Synthetic Data Sets | 1.0.1 | 1.0.1 |
sys Powerful and Reliable Tools for Running System Commands in R | 3.4.2 | 3.4.2 |
sysfonts Loading Fonts into R | 0.8.8 | 0.8.8 |
systemfit Estimating Systems of Simultaneous Equations | 1.1-30 | 1.1-30 |
systemfonts System Native Font Finding | 1.0.5 | 1.0.5 |
syt Young Tableaux | 0.5.0 | 0.5.0 |
tab Create Summary Tables for Statistical Reports | 5.1.1 | 5.1.1 |
tableone Create 'Table 1' to Describe Baseline Characteristics with or without Propensity Score Weights | 0.13.2 | 0.13.2 |
tables Formula-Driven Table Generation | 0.9.17 | 0.9.17 |
tabuSearch Tabu Search Algorithm for Binary Configurations | 1.1.1 | 1.1.1 |
tagcloud Tag Clouds | 0.6 | 0.6 |
tailDepFun Minimum Distance Estimation of Tail Dependence Models | 1.0.1 | 1.0.1 |
TAM Test Analysis Modules | 4.1-4 | 4.1-4 |
TAQMNGR Manage Tick-by-Tick Transaction Data | 2018.5-1 | 2018.5-1 |
targeted Targeted Inference | 0.4 | 0.4 |
targets Dynamic Function-Oriented 'Make'-Like Declarative Pipelines | 1.4.1 | 1.4.1 |
tau Text Analysis Utilities | 0.0-25 | 0.0-25 |
tbart Teitz and Bart's p-Median Algorithm | 1.0 | 1.0 |
tbrf Time-Based Rolling Functions | 0.1.5 | 0.1.5 |
tcltk Basic interface with tcl/tk | 4.4.1 | 4.4.1 |
tcltk2 Tcl/Tk Additions | 1.2-11 | 1.2-11 |
tclust Robust Trimmed Clustering | 1.5-5 | 1.5-5 |
Tcomp Data from the 2010 Tourism Forecasting Competition | 1.0.1 | 1.0.1 |
tcR Advanced Data Analysis of Immune Receptor Repertoires | 2.3.2 | 2.3.2 |
TDA Statistical Tools for Topological Data Analysis | 1.8.7 | 1.8.7 |
TDAstats Pipeline for Topological Data Analysis | 0.4.1 | 0.4.1 |
tdigest Wicked Fast, Accurate Quantiles Using t-Digests | 0.4.1 | 0.4.1 |
tdROC Nonparametric Estimation of Time-Dependent ROC, Brier Score, and Survival Difference from Right Censored Time-to-Event Data with or without Competing Risks | 2.0 | 2.0 |
tea Threshold Estimation Approaches | 1.1 | 1.1 |
TeachingDemos Demonstrations for Teaching and Learning | 2.12 | 2.12 |
TeachingSampling Selection of Samples and Parameter Estimation in Finite Population | 4.1.1 | 4.1.1 |
teamcolors Color Palettes for Pro Sports Teams | 0.0.4 | 0.0.4 |
teigen Model-Based Clustering and Classification with the Multivariate t Distribution | 2.2.2 | 2.2.2 |
telemac R Interface to the TELEMAC Model Suite | 0.1.1 | 0.1.1 |
tempdisagg Methods for Temporal Disaggregation and Interpolation of Time Series | 1.1.1 | 1.1.1 |
tensor Tensor product of arrays | 1.5 | 1.5 |
tensorA Advanced Tensor Arithmetic with Named Indices | 0.36.2 | 0.36.2 |
tensorBF Bayesian Tensor Factorization | 1.0.2 | 1.0.2 |
tensorflow R Interface to 'TensorFlow' | 2.15.0 | 2.15.0 |
tensorTS Factor and Autoregressive Models for Tensor Time Series | 1.0.1 | 1.0.1 |
TEQR Target Equivalence Range Design | 6.0-0 | 6.0-0 |
tergm Fit, Simulate and Diagnose Models for Network Evolution Based on Exponential-Family Random Graph Models | 4.2.0 | 4.2.0 |
term Create, Manipulate and Query Parameter Terms | 0.3.5 | 0.3.5 |
terra Spatial Data Analysis | 1.7-65 | 1.7-65 |
tesseract Open Source OCR Engine | 5.1.0 | 5.1.0 |
testcorr Testing Zero Correlation | 0.2.0 | 0.2.0 |
TestDataImputation Missing Item Responses Imputation for Test and Assessment Data | 2.3 | 2.3 |
TestDesign Optimal Test Design Approach to Fixed and Adaptive Test Construction | 1.5.1 | 1.5.1 |
tester Tests and Checks Characteristics of R Objects | 0.1.7 | 0.1.7 |
testextra Extract Test Blocks | 0.1.0.1 | 0.1.0.1 |
testit A Simple Package for Testing R Packages | 0.13 | 0.13 |
TestScorer GUI for Entering Test Items and Obtaining Raw and Transformed Scores | 1.7.2 | 1.7.2 |
testthat Unit Testing for R | 3.2.1 | 3.2.1 |
TexExamRandomizer Personalizes and Randomizes Exams Written in 'LaTeX' | 1.2.7 | 1.2.7 |
texmex Statistical Modelling of Extreme Values | 2.4.8 | 2.4.8 |
texPreview Compile and Preview Snippets of 'LaTeX' | 2.0.0 | 2.0.0 |
texreg Conversion of R Regression Output to LaTeX or HTML Tables | 1.39.3 | 1.39.3 |
text2vec Modern Text Mining Framework for R | 0.6.4 | 0.6.4 |
textcat N-Gram Based Text Categorization | 1.0-8 | 1.0-8 |
textir Inverse Regression for Text Analysis | 2.0-5 | 2.0-5 |
textplot Text Plots | 0.2.1 | 0.2.1 |
textrank Summarize Text by Ranking Sentences and Finding Keywords | 0.3.1 | 0.3.1 |
textreuse Detect Text Reuse and Document Similarity | 0.1.5 | 0.1.5 |
textshaping Bindings to the 'HarfBuzz' and 'Fribidi' Libraries for Text Shaping | 0.3.6 | 0.3.6 |
textTinyR Text Processing for Small or Big Data Files | 1.1.8 | 1.1.8 |
tfarima Transfer Function and ARIMA Models | 0.3.2 | 0.3.2 |
tfautograph Autograph R for 'Tensorflow' | 0.3.2 | 0.3.2 |
tfdatasets Interface to 'TensorFlow' Datasets | 2.17.0 | 2.17.0 |
tfdeploy Deploy 'TensorFlow' Models | 0.6.1 | 0.6.1 |
tfestimators Interface to 'TensorFlow' Estimators | 1.9.2 | 1.9.2 |
tfio Interface to 'TensorFlow IO' | 0.4.1 | 0.4.1 |
TFisher Optimal Thresholding Fisher's P-Value Combination Method | 0.2.0 | 0.2.0 |
TFMPvalue Efficient and Accurate P-Value Computation for Position Weight Matrices | 0.0.8 | 0.0.8 |
tfplot Time Frame User Utilities | 2021.6-1 | 2021.6-1 |
tframe Time Frame Coding Kernel | 2015.12-1.1 | 2015.12-1.1 |
tfruns Training Run Tools for 'TensorFlow' | 1.5.2 | 1.5.2 |
TFX R API to TrueFX(tm) | 0.1.0 | 0.1.0 |
tgp Bayesian Treed Gaussian Process Models | 2.4-22 | 2.4-22 |
TH.data TH's Data Archive | 1.1-2 | 1.1-2 |
theft Tools for Handling Extraction of Features from Time Series | 0.5.4.1 | 0.5.4.1 |
thematic Unified and Automatic 'Theming' of 'ggplot2', 'lattice', and 'base' R Graphics | 0.1.2.1 | 0.1.2.1 |
themis Extra Recipes Steps for Dealing with Unbalanced Data | 0.1.4 | 0.1.4 |
thief Temporal Hierarchical Forecasting | 0.3 | 0.3 |
ThreeArmedTrials Design and Analysis of Clinical Non-Inferiority or Superiority Trials with Active and Placebo Control | 1.0-3 | 1.0-3 |
threeBrain Your Advanced 3D Brain Visualization | 0.2.6 | 0.2.6 |
ThreeGroups ML Estimator for Baseline-Placebo-Treatment (Three-Group) Experiments | 0.21 | 0.21 |
threejs Interactive 3D Scatter Plots, Networks and Globes | 0.3.3 | 0.3.3 |
ThreeWay Three-Way Component Analysis | 1.1.3 | 1.1.3 |
thregI Threshold Regression for Interval-Censored Data with a Cure Rate Option | 1.0.4 | 1.0.4 |
threshr Threshold Selection and Uncertainty for Extreme Value Analysis | 1.0.5 | 1.0.5 |
thurstonianIRT Thurstonian IRT Models | 0.12.4 | 0.12.4 |
tibble Simple Data Frames | 3.2.1 | 3.2.1 |
tibbletime Time Aware Tibbles | 0.1.8 | 0.1.8 |
tictoc Functions for Timing R Scripts, as Well as Implementations of "Stack" and "StackList" Structures | 1.2 | 1.2 |
Tides Quasi-Periodic Time Series Characteristics | 2.1 | 2.1 |
tidyBdE Download Data from Bank of Spain | 0.3.5 | 0.3.5 |
tidycensus Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames | 1.5 | 1.5 |
tidygraph A Tidy API for Graph Manipulation | 1.3.1 | 1.3.1 |
tidyhydat Extract and Tidy Canadian 'Hydrometric' Data | 0.6.1 | 0.6.1 |
tidyLPA Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software | 1.1.0 | 1.1.0 |
tidymodels Easily Install and Load the 'Tidymodels' Packages | 1.1.1 | 1.1.1 |
tidypredict Run Predictions Inside the Database | 0.5 | 0.5 |
tidyquant Tidy Quantitative Financial Analysis | 1.0.7 | 1.0.7 |
tidyqwi A Convenient API for Accessing United States Census Bureau's Quarterly Workforce Indicator | 0.1.2 | 0.1.2 |
tidyr Tidy Messy Data | 1.3.1 | 1.3.1 |
tidyRSS Tidy RSS for R | 2.0.7 | 2.0.7 |
tidyselect Select from a Set of Strings | 1.2.0 | 1.2.0 |
tidysynth A Tidy Implementation of the Synthetic Control Method | 0.2.0 | 0.2.0 |
tidytable Tidy Interface to 'data.table' | 0.10.2 | 0.10.2 |
tidytext Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools | 0.4.1 | 0.4.1 |
tidytransit Read, Validate, Analyze, and Map GTFS Feeds | 1.3.1 | 1.3.1 |
tidytree A Tidy Tool for Phylogenetic Tree Data Manipulation | 0.4.6 | 0.4.6 |
tidyverse Easily Install and Load the 'Tidyverse' | 2.0.0 | 2.0.0 |
tiff Read and Write TIFF Images | 0.1-11 | 0.1-11 |
tigris Load Census TIGER/Line Shapefiles | 2.0.4 | 2.0.4 |
tikzDevice R Graphics Output in LaTeX Format | 0.12.6 | 0.12.6 |
tiler Create Geographic and Non-Geographic Map Tiles | 0.2.5 | 0.2.5 |
timechange Efficient Manipulation of Date-Times | 0.3.0 | 0.3.0 |
timeDate Rmetrics - Chronological and Calendar Objects | 4032.109 | 4032.109 |
TimeProjection Time Projections | 0.2.0 | 0.2.0 |
timereg Flexible Regression Models for Survival Data | 2.0.5 | 2.0.5 |
timeROC Time-Dependent ROC Curve and AUC for Censored Survival Data | 0.4 | 0.4 |
timesboot Bootstrap computations for time series objects | 1.0 | 1.0 |
timeSeries Financial Time Series Objects (Rmetrics) | 4032.109 | 4032.109 |
timeseriesdb A Time Series Database for Official Statistics with R and PostgreSQL | 1.0.0-1.1.2 | 1.0.0-1.1.2 |
timetk A Tool Kit for Working with Time Series | 2.9.0 | 2.9.0 |
TIMP Fitting Separable Nonlinear Models in Spectroscopy and Microscopy | 1.13.2 | 1.13.2 |
timsac Time Series Analysis and Control Package | 1.3.8-4 | 1.3.8-4 |
Tinflex A Universal Non-Uniform Random Number Generator | 2.4 | 2.4 |
tinylabels Lightweight Variable Labels | 0.2.4 | 0.2.4 |
tinyProject A Lightweight Template for Data Analysis Projects | 0.6.1 | 0.6.1 |
tinytable Simple and Configurable Tables in 'HTML', 'LaTeX', 'Markdown', 'Word', 'PNG', 'PDF', and 'Typst' Formats | 0.5.0 | 0.5.0 |
tinytest Lightweight and Feature Complete Unit Testing Framework | 1.4.1 | 1.4.1 |
tinytex Helper Functions to Install and Maintain TeX Live, and Compile LaTeX Documents | 0.49 | 0.49 |
tis Time Indexes and Time Indexed Series | 1.39 | 1.39 |
titrationCurves Acid/Base, Complexation, Redox, and Precipitation Titration Curves | 0.1.0 | 0.1.0 |
tkrplot TK Rplot | 0.0-26 | 0.0-26 |
TLMoments Calculate TL-Moments and Convert Them to Distribution Parameters | 0.7.5.3 | 0.7.5.3 |
tlrmvnmvt Low-Rank Methods for MVN and MVT Probabilities | 1.1.2 | 1.1.2 |
tm Text Mining Package | 0.7-11 | 0.7-11 |
tm.plugin.alceste Import Texts from Files in the 'Alceste' Format Using the 'tm' Text Mining Framework | 1.1 | 1.1 |
tm.plugin.dc Text Mining Distributed Corpus Plug-in | 0.2-10 | 0.2-10 |
tm.plugin.europresse Import Articles from 'Europresse' Using the 'tm' Text Mining Framework | 1.4 | 1.4 |
tm.plugin.factiva Import Articles from 'Factiva' Using the 'tm' Text Mining Framework | 1.8 | 1.8 |
tm.plugin.lexisnexis Import Articles from 'LexisNexis' Using the 'tm' Text Mining Framework | 1.4.1 | 1.4.1 |
tm.plugin.mail Text Mining E-Mail Plug-in | 0.2-2 | 0.2-2 |
tm.plugin.webmining Retrieve Structured, Textual Data from Various Web Sources | 1.3 | 1.3 |
tmap Thematic Maps | 3.3-4 | 3.3-4 |
tmaptools Thematic Map Tools | 3.1-1 | 3.1-1 |
TMB Template Model Builder: A General Random Effect Tool Inspired by 'ADMB' | 1.9.10 | 1.9.10 |
tmle Targeted Maximum Likelihood Estimation | 2.0.0 | 2.0.0 |
tmvmixnorm Sampling from Truncated Multivariate Normal and t Distributions | 1.1.1 | 1.1.1 |
tmvnsim Truncated Multivariate Normal Simulation | 1.0-2 | 1.0-2 |
tmvtnorm Truncated Multivariate Normal and Student t Distribution | 1.6 | 1.6 |
tokenizers Fast, Consistent Tokenization of Natural Language Text | 0.3.0 | 0.3.0 |
tokenizers.bpe Byte Pair Encoding Text Tokenization | 0.1.3 | 0.1.3 |
tolerance Statistical Tolerance Intervals and Regions | 2.0.0 | 2.0.0 |
tools | 4.4.1 | 4.4.1 |
tools4uplift Tools for Uplift Modeling | 1.0.0 | 1.0.0 |
toOrdinal Cardinal to Ordinal Number & Date Conversion | 1.3-0.0 | 1.3-0.0 |
topicdoc Topic-Specific Diagnostics for LDA and CTM Topic Models | 0.1.0 | 0.1.0 |
topicmodels Topic Models | 0.2-16 | 0.2-16 |
topmodel Implementation of the Hydrological Model TOPMODEL in R | 0.7.5 | 0.7.5 |
torch Tensors and Neural Networks with 'GPU' Acceleration | 0.12.0 | 0.12.0 |
toRvik Extensive and Tidy NCAA Men's College Basketball Data | 1.1.1 | 1.1.1 |
TouRnament Tools for Sports Competitions | 0.2.5 | 0.2.5 |
toxtestD Experimental design for binary toxicity tests | 2.0 | 2.0 |
TP.idm Estimation of Transition Probabilities for the Illness-Death Model | 1.5.1 | 1.5.1 |
TPmsm Estimation of Transition Probabilities in Multistate Models | 1.2.12 | 1.2.12 |
tpr Temporal Process Regression | 0.3-3 | 0.3-3 |
trackdem Particle Tracking and Demography | 0.6 | 0.6 |
trackdf Data Frame Class for Tracking Data | 0.3.2 | 0.3.2 |
trackeR Infrastructure for Running, Cycling and Swimming Data from GPS-Enabled Tracking Devices | 1.5.2 | 1.5.2 |
TrackReconstruction Reconstruct Animal Tracks from Magnetometer, Accelerometer, Depth and Optional Speed Data | 1.3 | 1.3 |
tractor.base Read, Manipulate and Visualise Magnetic Resonance Images | 3.3.5.1 | 3.3.5.1 |
trade Tools for Trade Practitioners | 0.8.0 | 0.8.0 |
trafo Estimation, Comparison and Selection of Transformations | 1.0.1 | 1.0.1 |
traipse Shared Tools for Tracking Data | 0.3.0 | 0.3.0 |
TrajDataMining Trajectories Data Mining | 0.1.6 | 0.1.6 |
trajectories Classes and Methods for Trajectory Data | 0.2-8 | 0.2-8 |
trajr Animal Trajectory Analysis | 1.5.1 | 1.5.1 |
tram Transformation Models | 1.0-0 | 1.0-0 |
TraMineR Trajectory Miner: a Sequence Analysis Toolkit | 2.2-10 | 2.2-10 |
transcribeR Automated Transcription of Audio Files Through the HP IDOL API | 0.0.0 | 0.0.0 |
transformr Polygon and Path Transformations | 0.1.3 | 0.1.3 |
TransModel Fit Linear Transformation Models for Right Censored Data | 2.3 | 2.3 |
TransPhylo Inference of Transmission Tree from a Dated Phylogeny | 1.4.5 | 1.4.5 |
transport Computation of Optimal Transport Plans and Wasserstein Distances | 0.14-6 | 0.14-6 |
tranSurv Transformation Model Based Estimation of Survival and Regression Under Dependent Truncation and Independent Censoring | 1.2.2 | 1.2.2 |
trapezoid The Trapezoidal Distribution | 2.0-2 | 2.0-2 |
trawl Estimation and Simulation of Trawl Processes | 0.2.2 | 0.2.2 |
tree Classification and Regression Trees | 1.0-43 | 1.0-43 |
TreeBUGS Hierarchical Multinomial Processing Tree Modeling | 1.5.0 | 1.5.0 |
treeClust Cluster Distances Through Trees | 1.1-7 | 1.1-7 |
treeio | 1.28.0 | 1.28.0 |
treemap Treemap Visualization | 2.4-4 | 2.4-4 |
treescape Statistical Exploration of Landscapes of Phylogenetic Trees | 1.10.18 | 1.10.18 |
TreeSim Simulating Phylogenetic Trees | 2.4 | 2.4 |
treespace Statistical Exploration of Landscapes of Phylogenetic Trees | 1.1.4.3 | 1.1.4.3 |
trekfont Star Trek Fonts Collection | 0.9.5 | 0.9.5 |
trend Non-Parametric Trend Tests and Change-Point Detection | 1.1.5 | 1.1.5 |
trendeval Evaluate Trending Models | 0.1.0 | 0.1.0 |
trending Model Temporal Trends | 0.1.0 | 0.1.0 |
TrendInTrend Odds Ratio Estimation and Power Calculation for the Trend in Trend Model | 1.1.3 | 1.1.3 |
TrialSize R Functions for Chapter 3,4,6,7,9,10,11,12,14,15 of Sample Size Calculation in Clinical Research | 1.4 | 1.4 |
triangle Distribution Functions and Parameter Estimates for the Triangle Distribution | 0.12 | 0.12 |
triebeard 'Radix' Trees in 'Rcpp' | 0.4.1 | 0.4.1 |
trimcluster Cluster Analysis with Trimming | 0.1-5 | 0.1-5 |
trip Tracking Data | 1.10.0 | 1.10.0 |
tripack Triangulation of Irregularly Spaced Data | 1.3-9.1 | 1.3-9.1 |
tripEstimation Metropolis Sampler and Supporting Functions for Estimating Animal Movement from Archival Tags and Satellite Fixes | 0.0-46 | 0.0-46 |
TripleR Social Relation Model (SRM) Analyses for Single or Multiple Groups | 1.5.4 | 1.5.4 |
trtf Transformation Trees and Forests | 0.4-2 | 0.4-2 |
TruncatedNormal Truncated Multivariate Normal and Student Distributions | 2.2.2 | 2.2.2 |
truncdist Truncated Random Variables | 1.0-2 | 1.0-2 |
truncnorm Truncated Normal Distribution | 1.0-9 | 1.0-9 |
truncreg Truncated Gaussian Regression Models | 0.2-5 | 0.2-5 |
truncSP Semi-parametric estimators of truncated regression models | 1.2.2 | 1.2.2 |
trust Trust Region Optimization | 0.1-8 | 0.1-8 |
trustOptim Trust Region Optimization for Nonlinear Functions with Sparse Hessians | 0.8.7.3 | 0.8.7.3 |
tryCatchLog Advanced 'tryCatch()' and 'try()' Functions | 1.3.1 | 1.3.1 |
TSA Time Series Analysis | 1.3.1 | 1.3.1 |
tsallisqexp Tsallis q-Exp Distribution | 0.9-4 | 0.9-4 |
TSANN Time Series Artificial Neural Network | 0.1.0 | 0.1.0 |
tsbox Class-Agnostic Time Series | 0.4.1 | 0.4.1 |
tsBSS Blind Source Separation and Supervised Dimension Reduction for Time Series | 1.0.0 | 1.0.0 |
TSclust Time Series Clustering Utilities | 1.3.1 | 1.3.1 |
TScompare 'TSdbi' Database Comparison | 2015.4-1 | 2015.4-1 |
tsdb Terribly-Simple Data Base for Time Series | 1.1-0 | 1.1-0 |
TSdbi Time Series Database Interface | 2017.4-1 | 2017.4-1 |
tsdecomp Decomposition of Time Series Data | 0.2 | 0.2 |
tsdisagg2 Time Series Disaggregation | 0.1.0 | 0.1.0 |
TSdisaggregation High-Dimensional Temporal Disaggregation | 2.0.0 | 2.0.0 |
TSdist Distance Measures for Time Series Data | 3.7.1 | 3.7.1 |
tsDyn Nonlinear Time Series Models with Regime Switching | 11.0.4 | 11.0.4 |
TSEntropies Time Series Entropies | 0.9 | 0.9 |
tseries Time Series Analysis and Computational Finance | 0.10-55 | 0.10-55 |
tseriesChaos Analysis of Nonlinear Time Series | 0.1-13.1 | 0.1-13.1 |
tseriesEntropy Entropy Based Analysis and Tests for Time Series | 0.7-2 | 0.7-2 |
tsfeatures Time Series Feature Extraction | 1.1.1 | 1.1.1 |
tsfknn Time Series Forecasting Using Nearest Neighbors | 0.6.0 | 0.6.0 |
TSHRC Two Stage Hazard Rate Comparison | 0.1-6 | 0.1-6 |
tsibble Tidy Temporal Data Frames and Tools | 1.1.4 | 1.1.4 |
tsibbledata Diverse Datasets for 'tsibble' | 0.4.1 | 0.4.1 |
tsibbletalk Interactive Graphics for Tsibble Objects | 0.1.0 | 0.1.0 |
tsintermittent Intermittent Time Series Forecasting | 1.10 | 1.10 |
tsiR An Implementation of the TSIR Model | 0.4.3 | 0.4.3 |
TSLSTM Long Short Term Memory (LSTM) Model for Time Series Forecasting | 0.1.0 | 0.1.0 |
TSMining Mining Univariate and Multivariate Motifs in Time-Series Data | 1.0 | 1.0 |
tsModel Time Series Modeling for Air Pollution and Health | 0.6-1 | 0.6-1 |
tsne T-Distributed Stochastic Neighbor Embedding for R (t-SNE) | 0.1-3.1 | 0.1-3.1 |
tsoutliers Detection of Outliers in Time Series | 0.6-10 | 0.6-10 |
TSP Traveling Salesperson Problem (TSP) | 1.2-4 | 1.2-4 |
tsPI Improved Prediction Intervals for ARIMA Processes and Structural Time Series | 1.0.4 | 1.0.4 |
tsrobprep Robust Preprocessing of Time Series Data | 0.3.2 | 0.3.2 |
tssim Simulation of Daily and Monthly Time Series | 0.1.7 | 0.1.7 |
TSstudio Functions for Time Series Analysis and Forecasting | 0.1.7 | 0.1.7 |
TSTutorial Fitting and Predict Time Series Interactive Laboratory | 1.2.7 | 1.2.7 |
tsutils Time Series Exploration, Modelling and Forecasting | 0.9.4 | 0.9.4 |
tswge Time Series for Data Science | 2.1.0 | 2.1.0 |
tth TeX-to-HTML/MathML Translators TtH/TtM | 4.12-0-1 | 4.12-0-1 |
TTmoment Sampling and Calculating the First and Second Moments for the Doubly Truncated Multivariate t Distribution | 1.0 | 1.0 |
TTR Technical Trading Rules | 0.24.4 | 0.24.4 |
ttutils Utility Functions | 1.0-1.1 | 1.0-1.1 |
tufte Tufte's Styles for R Markdown Documents | 0.11 | 0.11 |
tufterhandout Tufte-style html document format for rmarkdown | 1.2.1 | 1.2.1 |
tukeyGH Tukey's g-and-h Probability Distribution | 1.0.0 | 1.0.0 |
tune Tidy Tuning Tools | 1.1.2 | 1.1.2 |
tuneR Analysis of Music and Speech | 1.4.4 | 1.4.4 |
turboEM A Suite of Convergence Acceleration Schemes for EM, MM and Other Fixed-Point Algorithms | 2021.1 | 2021.1 |
TUWmodel Lumped/Semi-Distributed Hydrological Model for Education Purposes | 1.1-1 | 1.1-1 |
tvgeom The Time-Varying (Right-Truncated) Geometric Distribution | 1.0.1 | 1.0.1 |
tvm Time Value of Money Functions | 0.5.2 | 0.5.2 |
twang Toolkit for Weighting and Analysis of Nonequivalent Groups | 2.5 | 2.5 |
twangContinuous Toolkit for Weighting and Analysis of Nonequivalent Groups - Continuous Exposures | 1.0.0 | 1.0.0 |
twangMediation Twang Causal Mediation Modeling via Weighting | 1.2 | 1.2 |
tweedie Evaluation of Tweedie Exponential Family Models | 2.3.5 | 2.3.5 |
tweenr Interpolate Data for Smooth Animations | 2.0.2 | 2.0.2 |
twitteR R Based Twitter Client | 1.1.9 | 1.1.9 |
twosamples Fast Permutation Based Two Sample Tests | 2.0.1 | 2.0.1 |
txdbmaker | 1.0.1 | 1.0.1 |
tximeta | 1.16.0 | 1.16.0 |
tximport | 1.26.0 | 1.26.0 |
txtq A Small Message Queue for Parallel Processes | 0.2.4 | 0.2.4 |
tzdb Time Zone Database Information | 0.4.0 | 0.4.0 |
uaparserjs Parse 'User-Agent' Strings | 0.3.5 | 0.3.5 |
ubiquity PKPD, PBPK, and Systems Pharmacology Modeling Tools | 2.0.1 | 2.0.1 |
ucminf General-Purpose Unconstrained Non-Linear Optimization | 1.2.1 | 1.2.1 |
UComp Automatic Univariate Time Series Modelling of many Kinds | 4.0.2 | 4.0.2 |
UCSC.utils | 1.0.0 | 1.0.0 |
udpipe Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing with the 'UDPipe' 'NLP' Toolkit | 0.8.11 | 0.8.11 |
udunits2 Udunits-2 Bindings for R | 0.13.2 | 0.13.2 |
ufRisk Risk Measure Calculation in Financial TS | 1.0.7 | 1.0.7 |
ufs A Collection of Utilities | 0.5.10 | 0.5.10 |
ugatsdb Uganda Time Series Database API | 0.2.3 | 0.2.3 |
uGMAR Estimate Univariate Gaussian and Student's t Mixture Autoregressive Models | 3.4.5 | 3.4.5 |
ugomquantreg Quantile Regression Modeling for Unit-Gompertz Responses | 1.0.0 | 1.0.0 |
umap Uniform Manifold Approximation and Projection | 0.2.10.0 | 0.2.10.0 |
unbalanced Racing for Unbalanced Methods Selection | 2.0 | 2.0 |
uncmbb UNC Men's Basketball Match Results Since 1949-1950 Season | 0.2.2 | 0.2.2 |
uniah Unimodal Additive Hazards Model | 1.2 | 1.2 |
UnifiedDoseFinding Dose-Finding Methods for Non-Binary Outcomes | 0.1.10 | 0.1.10 |
uniqtag Abbreviate Strings to Short, Unique Identifiers | 1.0 | 1.0 |
uniReg Unimodal Penalized Spline Regression using B-Splines | 1.1 | 1.1 |
unisensR Read 'Unisens' Data | 0.3.3 | 0.3.3 |
unitizer Interactive R Unit Tests | 1.4.17 | 1.4.17 |
units Measurement Units for R Vectors | 0.8-4 | 0.8-4 |
universals S3 Generics for Bayesian Analyses | 0.0.5 | 0.0.5 |
univOutl Detection of Univariate Outliers | 0.4 | 0.4 |
UnivRNG Univariate Pseudo-Random Number Generation | 1.2.3 | 1.2.3 |
unmarked Models for Data from Unmarked Animals | 1.4.1 | 1.4.1 |
unrepx Analysis and Graphics for Unreplicated Experiments | 1.0-2 | 1.0-2 |
unrtf Extract Text from Rich Text Format (RTF) Documents | 1.4.5 | 1.4.5 |
untb Ecological Drift under the UNTB | 1.7-7 | 1.7-7 |
updog Flexible Genotyping for Polyploids | 2.1.2 | 2.1.2 |
UPMASK Unsupervised Photometric Membership Assignment in Stellar Clusters | 1.2 | 1.2 |
UpSetR A More Scalable Alternative to Venn and Euler Diagrams for Visualizing Intersecting Sets | 1.4.0 | 1.4.0 |
uptasticsearch Get Data Frame Representations of 'Elasticsearch' Results | 0.4.0 | 0.4.0 |
urca Unit Root and Cointegration Tests for Time Series Data | 1.3-3 | 1.3-3 |
urlchecker Run CRAN URL Checks from Older R Versions | 1.0.1 | 1.0.1 |
urlshorteneR R Wrapper for the 'Bit.ly' and 'Is.gd'/'v.gd' URL Shortening Services | 1.5.7 | 1.5.7 |
urltools Vectorised Tools for URL Handling and Parsing | 1.7.3 | 1.7.3 |
uroot Unit Root Tests for Seasonal Time Series | 2.1-3 | 2.1-3 |
usdata Data on the States and Counties of the United States | 0.2.0 | 0.2.0 |
useful A Collection of Handy, Useful Functions | 1.2.6 | 1.2.6 |
usethis Automate Package and Project Setup | 2.2.2 | 2.2.2 |
UsingR Data Sets, Etc. for the Text "Using R for Introductory Statistics", Second Edition | 2.0-7 | 2.0-7 |
usmap US Maps Including Alaska and Hawaii | 0.7.0 | 0.7.0 |
usmapdata Mapping Data for 'usmap' Package | 0.2.0 | 0.2.0 |
utf8 Unicode Text Processing | 1.2.4 | 1.2.4 |
utility Construct, Evaluate and Plot Value and Utility Functions | 1.4.6 | 1.4.6 |
utils | 4.4.1 | 4.4.1 |
uuid Tools for Generating and Handling of UUIDs | 1.2-0 | 1.2-0 |
uwot The Uniform Manifold Approximation and Projection (UMAP) Method for Dimensionality Reduction | 0.1.16 | 0.1.16 |
V8 Embedded JavaScript and WebAssembly Engine for R | 6.0.0 | 6.0.0 |
validate Data Validation Infrastructure | 1.1.3 | 1.1.3 |
validatetools Checking and Simplifying Validation Rule Sets | 0.5.2 | 0.5.2 |
vapour Access to the 'Geospatial Data Abstraction Library' ('GDAL') | 0.10.0 | 0.10.0 |
VAR.etp VAR Modelling: Estimation, Testing, and Prediction | 1.1 | 1.1 |
VARDetect Multiple Change Point Detection in Structural VAR Models | 0.1.6 | 0.1.6 |
vardiag Variogram Diagnostics | 0.2-1 | 0.2-1 |
vardpoor Variance Estimation for Sample Surveys by the Ultimate Cluster Method | 0.20.1 | 0.20.1 |
varhandle Functions for Robust Variable Handling | 2.0.6 | 2.0.6 |
variables Variable Descriptions | 1.1-1 | 1.1-1 |
VarianceGamma The Variance Gamma Distribution | 0.4-2 | 0.4-2 |
varImp RF Variable Importance for Arbitrary Measures | 0.4 | 0.4 |
vars VAR Modelling | 1.6-0 | 1.6-0 |
VarSelLCM Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values | 2.1.3.1 | 2.1.3.1 |
varSelRF Variable Selection using Random Forests | 0.7-8 | 0.7-8 |
VARshrink Shrinkage Estimation Methods for Vector Autoregressive Models | 0.3.1 | 0.3.1 |
VARsignR Sign Restrictions, Bayesian, Vector Autoregression Models | 0.1.3 | 0.1.3 |
VarSwapPrice Pricing a variance swap on an equity index | 1.0 | 1.0 |
vasicek Miscellaneous Functions for Vasicek Distribution | 0.0.3 | 0.0.3 |
VCA Variance Component Analysis | 1.4.5 | 1.4.5 |
vcd Visualizing Categorical Data | 1.4-13 | 1.4-13 |
vcdExtra 'vcd' Extensions and Additions | 0.8-5 | 0.8-5 |
vctrs Vector Helpers | 0.6.5 | 0.6.5 |
vdg Variance Dispersion Graphs and Fraction of Design Space Plots | 1.2.2 | 1.2.2 |
Vdgraph Variance Dispersion Graphs and Fraction of Design Space Plots for Response Surface Designs | 2.2-2 | 2.2-2 |
VdgRsm Plots of Scaled Prediction Variances for Response Surface Designs | 1.5 | 1.5 |
vdiffr Visual Regression Testing and Graphical Diffing | 1.0.2 | 1.0.2 |
vegan Community Ecology Package | 2.6-4 | 2.6-4 |
vegperiod Determine Thermal Vegetation Periods | 0.4.0 | 0.4.0 |
VennDiagram Generate High-Resolution Venn and Euler Plots | 1.7.3 | 1.7.3 |
venneuler Venn and Euler Diagrams | 1.1-4 | 1.1-4 |
VeryLargeIntegers Store and Operate with Arbitrarily Large Integers | 0.2.1 | 0.2.1 |
VGAM Vector Generalized Linear and Additive Models | 1.1-12 | 1.1-12 |
VIM Visualization and Imputation of Missing Values | 6.2.2 | 6.2.2 |
vimp Perform Inference on Algorithm-Agnostic Variable Importance | 2.2.5 | 2.2.5 |
VineCopula Statistical Inference of Vine Copulas | 2.5.0 | 2.5.0 |
vines Multivariate Dependence Modeling with Vines | 1.1.5 | 1.1.5 |
vioplot Violin Plot | 0.4.0 | 0.4.0 |
vip Variable Importance Plots | 0.4.1 | 0.4.1 |
vipor Plot Categorical Data Using Quasirandom Noise and Density Estimates | 0.4.7 | 0.4.7 |
viridis Colorblind-Friendly Color Maps for R | 0.6.5 | 0.6.5 |
viridisLite Colorblind-Friendly Color Maps (Lite Version) | 0.4.2 | 0.4.2 |
visdat Preliminary Visualisation of Data | 0.6.0 | 0.6.0 |
visNetwork Network Visualization using 'vis.js' Library | 2.1.2 | 2.1.2 |
vistributions Visualize Probability Distributions | 0.1.2 | 0.1.2 |
visualize Graph Probability Distributions with User Supplied Parameters and Statistics | 4.5.0 | 4.5.0 |
vitality Fitting Routines for the Vitality Family of Mortality Models | 1.3 | 1.3 |
vkR Access to VK API via R | 0.2 | 0.2 |
voi Expected Value of Information | 1.0.2 | 1.0.2 |
volleystat Detailed Statistics on Volleyball Matches | 0.2.0 | 0.2.0 |
vpc Create Visual Predictive Checks | 1.2.2 | 1.2.2 |
vroom Read and Write Rectangular Text Data Quickly | 1.6.5 | 1.6.5 |
vrtest Variance Ratio Tests and Other Tests for Martingale Difference Hypothesis | 1.2 | 1.2 |
vsd Graphical Shim for Visual Survival Data Analysis | 0.1.0 | 0.1.0 |
vsgoftest Goodness-of-Fit Tests Based on Kullback-Leibler Divergence | 1.0-1 | 1.0-1 |
vtable Variable Table for Variable Documentation | 1.3.4 | 1.3.4 |
W3CMarkupValidator R Interface to W3C Markup Validation Services | 0.1-7 | 0.1-7 |
wahc Autocorrelation and Heteroskedasticity Correction in Fixed Effect Panel Data Model | 1.0 | 1.0 |
waiter Loading Screen for 'Shiny' | 0.2.5 | 0.2.5 |
waldo Find Differences Between R Objects | 0.5.2 | 0.5.2 |
walrus Robust Statistical Methods | 1.0.5 | 1.0.5 |
warp Group Dates | 0.2.1 | 0.2.1 |
warpMix Mixed Effects Modeling with Warping for Functional Data Using B-Spline | 0.1.0 | 0.1.0 |
washdata Urban Water and Sanitation Survey Dataset | 0.1.3 | 0.1.3 |
WASP Wavelet System Prediction | 1.4.3 | 1.4.3 |
waterData Retrieval, Analysis, and Anomaly Calculation of Daily Hydrologic Time Series Data | 1.0.8 | 1.0.8 |
Watersheds Spatial Watershed Aggregation and Spatial Drainage Network Analysis | 1.1 | 1.1 |
WaveletComp Computational Wavelet Analysis | 1.1 | 1.1 |
wavelets Functions for Computing Wavelet Filters, Wavelet Transforms and Multiresolution Analyses | 0.3-0.2 | 0.3-0.2 |
WaveSampling Weakly Associated Vectors (WAVE) Sampling | 0.1.3 | 0.1.3 |
waveslim Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing | 1.8.4 | 1.8.4 |
wavethresh Wavelets Statistics and Transforms | 4.7.2 | 4.7.2 |
wavScalogram Wavelet Scalogram Tools for Time Series Analysis | 1.1.2 | 1.1.2 |
wbstats Programmatic Access to Data and Statistics from the World Bank API | 1.0.4 | 1.0.4 |
wbsts Multiple Change-Point Detection for Nonstationary Time Series | 2.1 | 2.1 |
wCorr Weighted Correlations | 1.9.8 | 1.9.8 |
WDI World Development Indicators and Other World Bank Data | 2.7.8 | 2.7.8 |
wdman 'Webdriver'/'Selenium' Binary Manager | 0.2.6 | 0.2.6 |
webchem Chemical Information from the Web | 1.3.0 | 1.3.0 |
webdriver 'WebDriver' Client for 'PhantomJS' | 1.0.6 | 1.0.6 |
webfakes Fake Web Apps for HTTP Testing | 1.1.3 | 1.1.3 |
WebGestaltR Gene Set Analysis Toolkit WebGestaltR | 0.4.6 | 0.4.6 |
webp A New Format for Lossless and Lossy Image Compression | 1.1.0 | 1.1.0 |
webreadr Tools for Reading Formatted Access Log Files | 0.4.0 | 0.4.0 |
webshot Take Screenshots of Web Pages | 0.5.5 | 0.5.5 |
webshot2 Take Screenshots of Web Pages | 0.1.0 | 0.1.0 |
websocket 'WebSocket' Client Library | 1.4.1 | 1.4.1 |
webutils Utility Functions for Developing Web Applications | 1.2.0 | 1.2.0 |
wehoop Access Women's Basketball Play by Play Data | 2.0.0 | 2.0.0 |
WeightedROC Fast, Weighted ROC Curves | 2020.1.31 | 2020.1.31 |
WeightIt Weighting for Covariate Balance in Observational Studies | 0.14.2 | 0.14.2 |
weightr Estimating Weight-Function Models for Publication Bias | 2.0.2 | 2.0.2 |
weights Weighting and Weighted Statistics | 1.0.4 | 1.0.4 |
WeightSVM Subject Weighted Support Vector Machines | 1.7-13 | 1.7-13 |
wellknown Convert Between 'WKT' and 'GeoJSON' | 0.7.4 | 0.7.4 |
welo Weighted and Standard Elo Rates | 0.1.3 | 0.1.3 |
WeMix Weighted Mixed-Effects Models Using Multilevel Pseudo Maximum Likelihood Estimation | 4.0.3 | 4.0.3 |
wevid Quantifying Performance of a Binary Classifier Through Weight of Evidence | 0.6.2 | 0.6.2 |
wgaim Whole Genome Average Interval Mapping for QTL Detection and Estimation using ASReml-R | 2.0-1 | 2.0-1 |
WhatIf Software for Evaluating Counterfactuals | 1.5-10 | 1.5-10 |
whisker {{mustache}} for R, Logicless Templating | 0.4 | 0.4 |
whitebox 'WhiteboxTools' R Frontend | 2.3.4 | 2.3.4 |
whoami Username, Full Name, Email Address, 'GitHub' Username of the Current User | 1.3.0 | 1.3.0 |
widgetframe 'Htmlwidgets' in Responsive 'iframes' | 0.3.1 | 0.3.1 |
WienR Derivatives of the First-Passage Time Density and Cumulative Distribution Function, and Random Sampling from the (Truncated) First-Passage Time Distribution | 0.3-15 | 0.3-15 |
wikipediatrend Public Subject Attention via Wikipedia Page View Statistics | 2.1.6 | 2.1.6 |
WilcoxCV Wilcoxon-based variable selection in cross-validation | 1.0-2 | 1.0-2 |
wildlifeDI Calculate Indices of Dynamic Interaction for Wildlife Tracking Data | 0.5.1 | 0.5.1 |
wildmeta Cluster Wild Bootstrapping for Meta-Analysis | 0.3.2 | 0.3.2 |
withr Run Code 'With' Temporarily Modified Global State | 3.0.0 | 3.0.0 |
wk Lightweight Well-Known Geometry Parsing | 0.9.1 | 0.9.1 |
wktmo Converting Weekly Data to Monthly Data | 1.0.5 | 1.0.5 |
wkutils Utilities for Well-Known Geometry Vectors | 0.1.3 | 0.1.3 |
wnl Minimization Tool for Pharmacokinetic-Pharmacodynamic Data Analysis | 0.7.3 | 0.7.3 |
womblR Spatiotemporal Boundary Detection Model for Areal Unit Data | 1.0.4 | 1.0.4 |
wooldridge 115 Data Sets from "Introductory Econometrics: A Modern Approach, 7e" by Jeffrey M. Wooldridge | 1.4-3 | 1.4-3 |
worcs Workflow for Open Reproducible Code in Science | 0.1.14 | 0.1.14 |
word2vec Distributed Representations of Words | 0.4.0 | 0.4.0 |
wordcloud Word Clouds | 2.6 | 2.6 |
wordcloud2 Create Word Cloud by 'htmlwidget' | 0.2.1 | 0.2.1 |
wordnet WordNet Interface | 0.1-16 | 0.1-16 |
workflowr A Framework for Reproducible and Collaborative Data Science | 1.7.1 | 1.7.1 |
workflows Modeling Workflows | 1.1.3 | 1.1.3 |
workflowsets Create a Collection of 'tidymodels' Workflows | 1.0.1 | 1.0.1 |
worldfootballR Extract and Clean World Football (Soccer) Data | 0.6.2 | 0.6.2 |
worldmet Import Surface Meteorological Data from NOAA Integrated Surface Database (ISD) | 0.9.8 | 0.9.8 |
wpp2017 World Population Prospects 2017 | 1.2-3 | 1.2-3 |
wpp2019 World Population Prospects 2019 | 1.1-1 | 1.1-1 |
wql Exploring Water Quality Monitoring Data | 1.0.0 | 1.0.0 |
wrangle A Systematic Data Wrangling Idiom | 0.6.3 | 0.6.3 |
wrapr Wrap R Tools for Debugging and Parametric Programming | 2.0.9 | 2.0.9 |
WrightMap IRT Item-Person Map with 'ConQuest' Integration | 1.3 | 1.3 |
writexl Export Data Frames to Excel 'xlsx' Format | 1.4.2 | 1.4.2 |
WriteXLS Cross-Platform Perl Based R Function to Create Excel 2003 (XLS) and Excel 2007 (XLSX) Files | 6.5.0 | 6.5.0 |
wrMisc Analyze Experimental High-Throughput (Omics) Data | 1.14.0 | 1.14.0 |
WRS2 A Collection of Robust Statistical Methods | 1.1-5 | 1.1-5 |
WRSS Water Resources System Simulator | 3.0 | 3.0 |
wrswoR Weighted Random Sampling without Replacement | 1.1.1 | 1.1.1 |
WRTDStidal Weighted Regression for Water Quality Evaluation in Tidal Waters | 1.1.4 | 1.1.4 |
wsrf Weighted Subspace Random Forest for Classification | 1.7.30 | 1.7.30 |
WufooR R Wrapper for the 'Wufoo.com' - The Form Building Service | 1.0.1 | 1.0.1 |
x12 Interface to 'X12-ARIMA'/'X13-ARIMA-SEATS' and Structure for Batch Processing of Seasonal Adjustment | 1.10.3 | 1.10.3 |
x13binary Provide the 'x13ashtml' Seasonal Adjustment Binary | 1.1.57-3 | 1.1.57-3 |
xaringan Presentation Ninja | 0.28 | 0.28 |
XBRL Extraction of Business Financial Information from 'XBRL' Documents | 0.99.19.1 | 0.99.19.1 |
xfun Supporting Functions for Packages Maintained by 'Yihui Xie' | 0.41 | 0.41 |
xgboost Extreme Gradient Boosting | 1.7.6.1 | 1.7.6.1 |
xmeta A Toolbox for Multivariate Meta-Analysis | 1.3.2 | 1.3.2 |
XML Tools for Parsing and Generating XML Within R and S-Plus | 3.99-0.16.1 | 3.99-0.16.1 |
xml2 Parse XML | 1.3.6 | 1.3.6 |
xmlparsedata Parse Data of 'R' Code as an 'XML' Tree | 1.0.5 | 1.0.5 |
xmlrpc2 Implementation of the Remote Procedure Call Protocol ('XML-RPC') | 1.1 | 1.1 |
xopen Open System Files, 'URLs', Anything | 1.0.0 | 1.0.0 |
xplain Providing Interactive Interpretations and Explanations of Statistical Results | 0.2.2 | 0.2.2 |
xplorerr Tools for Interactive Data Exploration | 0.1.2 | 0.1.2 |
xpose Diagnostics for Pharmacometric Models | 0.4.17 | 0.4.17 |
xpose.nlmixr Graphical Diagnostics for Pharmacometric Models: Extension to 'nlmixr' | 0.3.0 | 0.3.0 |
XR A Structure for Interfaces from R | 0.7.2 | 0.7.2 |
XRJulia Structured Interface to Julia | 0.9.0 | 0.9.0 |
XRPython Structured Interface to 'Python' | 0.8 | 0.8 |
xtable Export Tables to LaTeX or HTML | 1.8-4 | 1.8-4 |
xts eXtensible Time Series | 0.13.2 | 0.13.2 |
XVector | 0.44.0 | 0.44.0 |
xxIRT Item Response Theory and Computer-Based Testing in R | 2.1.2 | 2.1.2 |
yaImpute Nearest Neighbor Observation Imputation and Evaluation Tools | 1.0-34 | 1.0-34 |
yaml Methods to Convert R Data to YAML and Back | 2.3.10 | 2.3.10 |
yardstick Tidy Characterizations of Model Performance | 1.3.0 | 1.3.0 |
yesno Ask Yes-No Questions | 0.1.3 | 0.1.3 |
yhatr R Binder for the Yhat API | 0.15.1 | 0.15.1 |
ympes Collection of Helper Functions | 1.5.0 | 1.5.0 |
yorkr Analyze Cricket Performances Based on Data from Cricsheet | 0.0.41 | 0.0.41 |
YPmodel The Short-Term and Long-Term Hazard Ratio Model for Survival Data | 1.4 | 1.4 |
yuima The YUIMA Project Package for SDEs | 1.15.22 | 1.15.22 |
yulab.utils Supporting Functions for Packages Maintained by 'YuLab-SMU' | 0.1.4 | 0.1.4 |
yum Utilities to Extract and Process 'YAML' Fragments | 0.1.0 | 0.1.0 |
zCompositions Treatment of Zeros, Left-Censored and Missing Values in Compositional Data Sets | 1.5.0-1 | 1.5.0-1 |
zcurve An Implementation of Z-Curves | 2.4.0 | 2.4.0 |
zeallot Multiple, Unpacking, and Destructuring Assignment | 0.1.0 | 0.1.0 |
Zelig Everyone's Statistical Software | 5.1.7 | 5.1.7 |
ZeligChoice Zelig Choice Models | 0.9-6 | 0.9-6 |
ZeligEI Zelig Ecological Inference Models | 0.1-2 | 0.1-2 |
zeligverse Easily Install and Load Stable Zelig Packages | 0.1.1 | 0.1.1 |
zic Bayesian Inference for Zero-Inflated Count Models | 0.9.1 | 0.9.1 |
ZIM Zero-Inflated Models (ZIM) for Count Time Series with Excess Zeros | 1.1.0 | 1.1.0 |
ZINARp Simulate INAR/ZINAR(p) Models and Estimate Its Parameters | 0.1.0 | 0.1.0 |
zip Cross-Platform 'zip' Compression | 2.3.0 | 2.3.0 |
zipfextR Zipf Extended Distributions | 1.0.2 | 1.0.2 |
zipfR Statistical Models for Word Frequency Distributions | 0.6-70 | 0.6-70 |
zlib Compression and Decompression | 1.0.3 | 1.0.3 |
zlibbioc | 1.50.0 | 1.50.0 |
zoo S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations) | 1.8-12 | 1.8-12 |
zoom A Spatial Data Visualization Tool | 2.0.6 | 2.0.6 |
zoon Reproducible, Accessible & Shareable Species Distribution Modelling | 0.6.5 | 0.6.5 |
ZRA Dynamic Plots for Time Series Forecasting | 0.2 | 0.2 |
Zseq Integer Sequence Generator | 0.2.1 | 0.2.1 |
ztable Zebra-Striped Tables in LaTeX and HTML Formats | 0.2.3 | 0.2.3 |
zTree Functions to Import Data from 'z-Tree' into R | 1.0.7 | 1.0.7 |
zyp Zhang + Yue-Pilon Trends Package | 0.11-1 | 0.11-1 |
This information was collected at 20241112-1146.