This package provides functions for regulation, decomposition and analysis of space-time series. The pastecs library is a PNEC-Art4 and IFREMER initiative to bring PASSTEC 2000 functionalities to R.

This package provides some helpful extensions and modifications to the ggplot2 package to combine multiple ggplot2 plots into one and label them with letters, as is often required for scientific publications.

This package provides a generalized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link. It can also handle clustered categorical responses.

This package detects statistically significant differences between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD).

Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups.

Haplotype simulations of rare variant genetic data that emulates real data can be performed with RAREsim. RAREsim uses the expected number of variants in MAC bins - either as provided by default parameters or estimated from target data - and an abundance of rare variants as simulated HAPGEN2 to probabilistically prune variants. RAREsim produces haplotypes that emulate real sequencing data with respect to the total number of variants, allele frequency spectrum, haplotype structure, and variant annotation.

This package defines data structures for linkage disequilibrium (LD) measures in populations. Its purpose is to simplify handling of existing population-level data for the purpose of flexibly defining LD blocks.

This package provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. It also provides some helper functions to assist development of other packages.

This package is a collection of functions and layers to enhance ggplot2. The flagship function is ggMarginal(), which can be used to add marginal histograms/boxplots/density plots to ggplot2 scatterplots.

The fstlib library provides multithreaded serialization of compressed data frames using the fst format. The fst format allows for random access of stored data and compression with the LZ4 and ZSTD compressors.

This package provides functions for reading and writing data stored by some versions of Epi Info, Minitab, S, SAS, SPSS, Stata, Systat and Weka and for reading and writing some dBase files.

This package helps users to work with TF metadata from various sources. Significant catalogs of TFs and classifications thereof are made available. Tools for working with motif scans are also provided.

Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes.

This package performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

This package provides a common interface to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. R, Spark, Stan, etc).

The feature package contains functions to display and compute kernel density estimates, significant gradient and significant curvature regions. Significant gradient and/or curvature regions often correspond to significant features (e.g. local modes).

This package provides fast and accurate convolution-type smoothed quantile regression, implemented using Barzilai-Borwein gradient descent with a Huber regression warm start. Confidence intervals for regression coefficients are constructed using multiplier bootstrap.

This package provides functions to fit kernel density functions to data on temporal activity patterns of animals; estimate coefficients of overlapping of densities for two species; and calculate bootstrap estimates of confidence intervals.

This package provides the asynchronous RPC client-server framework and message specification for Rigetti Quantum Cloud Services (QCS). It implements an efficient transport protocol by using ZeroMQ (ZMQ) sockets and MessagePack (msgpack) serialization.

This package wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results.

This package provides methods for cluster analysis. It is a much extended version of the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".

This R package contains examples from the book Regression for Categorical Data, Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.

This package simulates the process of installing a package and then attaching it. This is a key part of the devtools package as it allows you to rapidly iterate while developing a package.

This package implements list environments. List environments are environments that have list-like properties. For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting.