Render R Markdown to Markdown (without using knitr), and Markdown to lightweight HTML or LaTeX documents with the commonmark package (instead of Pandoc). Some missing Markdown features in commonmark are also supported, such as raw HTML or LaTeX blocks, LaTeX math, superscripts, subscripts, footnotes, element attributes, and appendices, but not all Pandoc Markdown features are (or will be) supported. With additional JavaScript and CSS, you can also create HTML slides and articles. This package can be viewed as a trimmed-down version of R Markdown and knitr. It does not aim at rich Markdown features or a large variety of output formats (the primary formats are HTML and LaTeX). Book and website projects of multiple input documents are also supported.

Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors.

This package provides functionality to benchmark your CPU and compare against other CPUs. Also provides functions for obtaining system specifications, such as RAM, CPU type, and R version.

This package extends sparse matrix and vector classes from the Matrix package by providing:

1. Methods and operators that work natively on CSR formats (compressed sparse row, a.k.a. RsparseMatrix) such as slicing/sub-setting, assignment, rbind(), mathematical operators for CSR and COO such as addition or sqrt(), and methods such as diag();

2. Multi-threaded matrix multiplication and cross-product for many <sparse, dense> types, including the float32 type from float;

3. Coercion methods between pairs of classes which are not present in Matrix, such as from dgCMatrix to ngRMatrix, as well as convenience conversion functions;

4. Utility functions for sparse matrices such as sorting the indices or removing zero-valued entries;

5. Fast transposes that work by outputting in the opposite storage format;

6. Faster replacements for many Matrix methods for all sparse types, such as slicing and elementwise multiplication.

7. Convenience functions for sparse objects, such as mapSparse or a shorter show method.

This package converts between GeoJSON and Simple Feature objects.

The Rcpp package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about Rcpp is provided by several vignettes included in this package, via the Rcpp Gallery site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, JSS), and the book by Eddelbuettel (2013, Springer); see citation("Rcpp") for details on these last two.

This package supports the analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003).

The DHARMa package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as JAGS, STAN, or BUGS can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial, phylogenetic and temporal autocorrelation.

This package helps you to automate R package and project setup tasks that are otherwise performed manually. This includes setting up unit testing, test coverage, continuous integration, Git, GitHub integration, licenses, Rcpp, RStudio projects, and more.

This package provides tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction.

This package provides functions to calculate: moments, Pearson's kurtosis, Geary's kurtosis and skewness; it also includes tests related to them (Anscombe-Glynn, D'Agostino, Bonett-Seier).

This package performs projection predictive feature selection for generalized linear models and generalized linear and additive multilevel models. The package is compatible with the rstanarm and brms packages, but other reference models can also be used. See the package vignette for more information and examples.

Radix trees, or tries, are key-value data structures optimized for efficient lookups, similar in purpose to hash tables. This package provides an implementation of radix trees for use in R programming and in developing packages with Rcpp.

This package provides an improved heatmap package. It is completely compatible with the original R function heatmap, and provides more powerful and convenient features.

This package provides tools for categorical data analysis with complete or missing responses.

This package provides a set of functions to generate high-resolution Venn and Euler plots. It includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.

This package provides vector map data from https://www.naturalearthdata.com/. Access functions are provided in the accompanying package rnaturalearth.

This package provides a drop-in replacement for rasterize from the raster package that takes sf-type objects, and is much faster. There is support for the main options provided by the rasterize function, including setting the field used and background value, and options for aggregating multi-layer rasters.

This package lets you import Excel files into R. It supports .xls via the embedded libxls C library and .xlsx via the embedded RapidXML C++ library.

This package provides a URL-safe base64 encoder and decoder. In contrast to RFC3548, the 62nd character (+) is replaced with -, the 63rd character (/) is replaced with _. Furthermore, the encoder does not fill the string with trailing =. The resulting encoded strings comply to the regular expression pattern [A-Za-z0-9_-] and thus are safe to use in URLs or for file names. The package also comes with a simple base32 encoder/decoder suited for case insensitive file systems.

This package computes the Kendall rank correlation and Mann-Kendall trend test.

This package provides tools to interact with Google Sheets from within R.

This package provides a parallel estimation of the mutual information based on entropy estimates from k-nearest neighbors distances and algorithms for the reconstruction of gene regulatory networks.

This package provides a collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future.