This is a package for variable elimination (Gaussian elimination, Fourier-Motzkin elimination), Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution, projecting a vector on the convex polytope described by a system of (in)equations, simplify systems by removing spurious columns and rows and collapse implied equalities, test if a matrix is totally unimodular, compute variable ranges implied by linear (in)equalities.

This package provides an interface to Amazon Web Services database services, including Relational Database Service (RDS), DynamoDB NoSQL database, and more.

This package is for building isoscapes using mixed models and inferring the geographic origin of samples based on their isotopic ratios. This package is essentially a simplified interface to several other packages which implements a new statistical framework based on mixed models. It uses spaMM for fitting and predicting isoscapes, and assigning an organism's origin depending on its isotopic ratio. IsoriX also relies heavily on the package rasterVis for plotting the maps produced with terra using lattice'.

This package provides a set of tools for the statistical analysis of data using:

1. normal linear models;

2. generalized linear models;

3. negative binomial regression models as alternative to the Poisson regression models under the presence of overdispersion;

4. beta-binomial and random-clumped binomial regression models as alternative to the binomial regression models under the presence of overdispersion;

5. zero-inflated and zero-altered regression models to deal with zero-excess in count data;

6. generalized nonlinear models;

7. generalized estimating equations for cluster correlated data.

This package is a usability wrapper around snow for easier development of parallel R programs. This package offers e.g. extended error checks, and additional functions. All functions work in sequential mode, too, if no cluster is present or wished. The package is also designed as connector to the cluster management tool sfCluster, but can also used without it.

This package provides a collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values.

This package provides an R interface to the JAGS MCMC library. JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation.

This package provides an R interface to the libgit2 library, which is a pure C implementation of the Git core methods.

The tidyverse is a set of packages that work in harmony because they share common data representations and API design. This package is designed to make it easy to install and load multiple tidyverse packages in a single step.

This package provides a more scalable alternative to Venn and Euler diagrams for visualizing intersecting sets. Create visualizations of intersecting sets using a novel matrix design, along with visualizations of several common set, element and attribute related tasks.

This package provides datasets to accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage.

Partition a data frame across multiple worker processes to provide simple multicore parallelism.

The goal of pak is to make package installation faster and more reliable. In particular, it performs all HTTP operations in parallel, so metadata resolution and package downloads are fast. Metadata and package files are cached on the local disk as well. pak has a dependency solver, so it finds version conflicts before performing the installation. This version of pak supports CRAN, Bioconductor and GitHub packages as well.

This package creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. It provides diverse quality criteria and utility functions for the class design, which is also used by other packages for designed experiments.

This package generates graphics with embedded details from statistical tests. Statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous or categorical data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses.

This package provides a forest plot that allows for multiple confidence intervals per row, custom fonts for each text element, custom confidence intervals, text mixed with expressions, and more. The aim is to extend the use of forest plots beyond meta-analyses. This is a more general version of the original rmeta package's forestplot() function and relies heavily on the grid package.

This package enhances the R Optimization Infrastructure (ROI) package with the Embedded Conic Solver (ECOS) for solving conic optimization problems.

This package provides tools to generate a violin point plot, a combination of a violin/histogram plot and a scatter plot by offsetting points within a category based on their density using quasirandom noise.

Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is Gaussianize, which works similarly to scale, but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x MyFavoriteDistribution and use it in their analysis right away.

This package covers many important models used in marketing and micro-econometrics applications, including Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity, and Bayesian Analysis of Aggregate Random Coefficient Logit Models.

This package can be used to solve Linear Programming / Linear Optimization problems by using the simplex algorithm.

Network Common Data Form (netCDF) files are widely used for scientific data. Library-level access in R is provided through packages RNetCDF and ncdf4. The package ncdfCF is built on top of RNetCDF and makes the data and its attributes available as a set of R6 classes that are informed by the Climate and Forecasting Metadata Conventions. Access to the data uses standard R subsetting operators and common function forms.

Computes local polynomial estimators for the regression and also density. It comprises several different utilities to handle kernel estimators.

Extract metadata from NetCDF data sources; these can be files, file handles or servers. This package leverages and extends the lower level functions of the RNetCDF package providing a consistent set of functions that all return data frames.