The package offers functions for analyzing and interactively exploring large-scale single-cell RNA-seq datasets. Pagoda2 primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. pagoda2 was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos.

GAMs, GAMMs and other generalized ridge regression with multiple smoothing parameter estimation by GCV, REML or UBRE/AIC. The library includes a gam() function, a wide variety of smoothers, JAGS support and distributions beyond the exponential family.

This package aims to provide easy-to-use, efficient, flexible and scalable statistical tools. It provides and uses file-backed big matrices via memory-mapping. It provides for instance matrix operations, Principal Component Analysis, sparse linear supervised models, utility functions and more.

This package can automatically extract statistical null-hypothesis significant testing (NHST) results from articles and recompute the p-values based on the reported test statistic and degrees of freedom to detect possible inconsistencies.

Online data collection tools like Google Forms often export multiple-response questions with data concatenated in cells. The concat.split (cSplit) family of functions provided by this package splits such data into separate cells. This package also includes functions to stack groups of columns and to reshape wide data, even when the data are "unbalanced"---something which reshape (from base R) does not handle, and which melt and dcast from reshape2 do not easily handle.

This package extends shinydashboard with AdminLTE2 components. AdminLTE2 is a Bootstrap 3 dashboard template. Customize boxes, add timelines and a lot more.

This package provides an up-to-date copy of the Internet Assigned Numbers Authority (IANA) Time Zone Database. It is updated periodically to reflect changes made by political bodies to time zone boundaries, UTC offsets, and daylight saving time rules. Additionally, this package provides a C++ interface for working with the date library. date provides comprehensive support for working with dates and date-times, which this package exposes to make it easier for other R packages to utilize. Headers are provided for calendar specific calculations, along with a limited interface for time zone manipulations.

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.

This package contains functions useful for correlation theory, meta-analysis (validity-generalization), reliability, item analysis, inter-rater reliability, and classical utility.

This package provides functions, data sets, analyses and examples from the third edition of the book A Handbook of Statistical Analyses Using R (Torsten Hothorn and Brian S. Everitt, Chapman & Hall/CRC, 2014). The first chapter of the book, which is entitled An Introduction to R, is completely included in this package, for all other chapters, a vignette containing all data analyses is available. In addition, Sweave source code for slides of selected chapters is included in this package.

This package provides an R wrapper for the special functions and quasi random number generators of the GNU Scientific Library.

This package provides functionality to define and train neural networks similar to PyTorch but written entirely in R using the libtorch library. It also supports low-level tensor operations and GPU acceleration.

This package provides an R interface to V8: Google's JavaScript and WebAssembly engine.

This package provides a language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety.

This package implements various estimators of entropy, such as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood and the Millow-Madow estimator, various Bayesian estimators, and the Chao-Shen estimator. It also offers an R interface to the NSB estimator. Furthermore, it provides functions for estimating Kullback-Leibler divergence, chi-squared, mutual information, and chi-squared statistic of independence. In addition there are functions for discretizing continuous random variables.

This package provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.

Reshape2 is an R library to flexibly restructure and aggregate data using just two functions: melt and dcast (or acast).

This package provides functions to compute the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm.

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

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

This package allows for the estimation of a wide variety of advanced multivariate statistical models. It consists of a library of functions and optimizers that allow you to quickly and flexibly define an SEM model and estimate parameters given observed data.

This package creates scatterpie plots, especially useful for plotting pies on a map.

This package provides a toolset for the exploration of genetic and genomic data. Adegenet provides formal (S4) classes for storing and handling various genetic data, including genetic markers with varying ploidy and hierarchical population structure (genind class), alleles counts by populations (genpop), and genome-wide SNP data (genlight). It also implements original multivariate methods (DAPC, sPCA), graphics, statistical tests, simulation tools, distance and similarity measures, and several spatial methods. A range of both empirical and simulated datasets is also provided to illustrate various methods.

This package implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.