This package implements a data structure similar to hashes in Perl and dictionaries in Python but with a purposefully R flavor. For objects of appreciable size, access using hashes outperforms native named lists and vectors.

This package lets you expand factors, characters and other eligible classes into dummy/indicator variables.

This package provides a normalization method for single-cell UMI count data using a variance stabilizing transformation. The transformation is based on a negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction.

This package provides the functionality to set configuration options on a per-package basis. Options set by a given package only apply to that package, other packages are unaffected.

This package provides an implementation of interpreted string literals, inspired by Python's Literal String Interpolation (PEP-0498) and Docstrings (PEP-0257) and Julia's Triple-Quoted String Literals.

Apache Arrow is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. This package provides an R interface to the Arrow C++ library.

This package is an implementation of a regularized regression prediction and empirical Bayes method to recover the true gene expression profile in noisy and sparse single-cell RNA-seq data. In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of genes with low or moderate expression, which hinders downstream analysis. This package single-cell analysis via expression recovery (SAVER) implements an expression recovery method for unique molecule index (UMI)-based scRNA-seq data that borrows information across genes and cells to provide accurate expression estimates for all genes.

This is a package for maximum likelihood estimation of random utility discrete choice models. The software is described in Croissant (2020) <doi:10.18637/jss.v095.i11> and the underlying methods in Train (2009) <doi:10.1017/CBO9780511805271>.

This package provides methods and functions for fitting maximum likelihood models in R. This package modifies and extends the mle classes in the stats4 package.

This package provides statistical models of biased sampling in the form of univariate and multivariate noncentral hypergeometric distributions, including Wallenius' noncentral hypergeometric distribution and Fisher's noncentral hypergeometric distribution (also called extended hypergeometric distribution).

This package provides utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data. In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data.

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

This package provides a collection of functions to implement a class for univariate polynomial manipulations.

This package provides a way to read, write and display bitmap images stored in the JPEG format with R. It can read and write both files and in-memory raw vectors.

This package provides methods for fast access to large ASCII files. Currently the following file formats are supported: comma separated format (CSV) and fixed width format. It is assumed that the files are too large to fit into memory, although the package can also be used to efficiently access files that do fit into memory. Methods are provided to access and process files blockwise. Furthermore, an opened file can be accessed as one would an ordinary data.frame. The LaF vignette gives an overview of the functionality provided.

This package provides output formats and utilities for authoring books and technical documents with R Markdown.

Gtable is a collection of tools to make it easier to work with "tables" of grobs.

This package contains functions to estimate L-moments and trimmed L-moments from the data. It also contains functions to estimate the parameters of the normal polynomial quantile mixture and the Cauchy polynomial quantile mixture from L-moments and trimmed L-moments.

This package aims to identify candidate genes that are differentially methylated between cases and controls. It applies Student's t-test and delta beta analysis to identify candidate genes containing multiple CpG sites.

This package provides a derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell.

This package provides functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology.

Call job::job(<code here>) to run R code as an RStudio job and keep your console free in the meantime. This allows for a productive workflow while testing (multiple) long-running chunks of code. It can also be used to organize results using the RStudio Jobs GUI or to test code in a clean environment. Two RStudio Addins can be used to run selected code as a job.

Ggdag is built on top of dagitty, an R package that uses the DAGitty web tool for creating and analyzing DAGs. ggdag makes it easy to tidy and plot dagitty objects using ggplot2 and ggraph, as well as common analytic and graphical functions, such as determining adjustment sets and node relationships.

This package provides an R interface to Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen and Guestrin (2016). The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.