The clusterGeneration package provides functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables.

This package provides a fast match replacement for cases that require repeated look-ups. It is slightly faster that R's built-in match function on first match against a table, but extremely fast on any subsequent lookup as it keeps the hash table in memory.

This package is an R package designed for QC, analysis, and exploration of single cell RNA-seq data. It easily enables widely-used analytical techniques, including the identification of highly variable genes, dimensionality reduction; PCA, ICA, t-SNE, standard unsupervised clustering algorithms; density clustering, hierarchical clustering, k-means, and the discovery of differentially expressed genes and markers.

This package provides an optimization method based on sequential quadratic programming for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithm is expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver, and they are expected to arrive at solutions more quickly when the number of samples is large and the number of mixture components is not too large.

Full 64-bit resolution date and time functionality with nanosecond granularity is provided, with easy transition to and from the standard POSIXct type. Three additional classes offer interval, period and duration functionality for nanosecond-resolution timestamps.

This package provides an extension to the Shiny web application framework for R, making it easy to create attractive dashboards.

This package provides functions for robust principal component analysis (PCA) by projection pursuit.

This package provides R bindings to the uchardet encoding detector library from Mozilla. It takes a sequence of bytes in an unknown character encoding without any additional information, and attempts to get the encoding of the text. All return names of the encodings are iconv-compatible.

This package provides smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2017). Differently from quantreg, the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in the mgcv package, but fits non-parametric quantile regression models.

This package provides tools for fitting linear models and generalized linear models to large data sets by updating algorithms.

This package provides a set of predicates and assertions for checking the properties of files and connections. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

The clusterCrit package provides an implementation of the following indices: Czekanowski-Dice, Folkes-Mallows, Hubert Γ, Jaccard, McNemar, Kulczynski, Phi, Rand, Rogers-Tanimoto, Russel-Rao or Sokal-Sneath. ClusterCrit defines several functions which compute internal quality indices or external comparison indices. The partitions are specified as an integer vector giving the index of the cluster each observation belongs to.

This package provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure.

This package provides a collection of high-performance utilities. It can be used to compute distances, correlations, autocorrelations, clustering, and other tasks. It also contains a graph clustering algorithm described in MetaCell analysis of single-cell RNA-seq data using K-nn graph partitions.

This package provides software and data for the book "An Introduction to the Bootstrap" by B. Efron and R. Tibshirani, 1993, Chapman and Hall. This package is primarily provided for projects already based on it, and for support of the book. New projects should preferentially use the recommended package "boot".

This package provides tools for accessing the Botanical Information and Ecology Network (BIEN) database. The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data. This package provides functions that query the BIEN database by constructing and executing optimized SQL queries.

This package provides the means to compile user-supplied C++ functions with Rcpp and retrieve an XPtr that can be passed to other C++ components.

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 functions to perform reproducible parallel foreach loops, using independent random streams as generated by L'Ecuyer's combined multiple-recursive generator. It enables to easily convert standard %dopar% loops into fully reproducible loops, independently of the number of workers, the task scheduling strategy, or the chosen parallel environment and associated foreach backend.

This is a package for developers to check user-supplied function arguments. It is designed to be simple, fast and customizable. Error messages follow the tidyverse style guide.

This is an alternative mechanism for importing objects from packages. The syntax allows for importing multiple objects from a package with a single command in an expressive way. The import package bridges some of the gap between using library (or require) and direct (single-object) imports. Furthermore the imported objects are not placed in the current environment. It is also possible to import objects from stand-alone .R files.

This package offers an easy to use way to draw a Venn diagram with ggplot2.

This package can be used to conduct post hoc analyses of resampling results generated by models. For example, if two models are evaluated with the root mean squared error (RMSE) using 10-fold cross-validation, there are 10 paired statistics. These can be used to make comparisons between models without involving a test set.

Hapassoc performs likelihood inference of trait associations with haplotypes and other covariates in generalized linear models (GLMs). The functions are developed primarily for data collected in cohort or cross-sectional studies. They can accommodate uncertain haplotype phase and handle missing genotypes at some SNPs.