This package provides an integration of base and grid graphics for R.

This package provides an R implementation of an extension of the BayeScan software for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection.

This package provides a set of utilities for client/server computing with R, controlling a remote R session (the server) from a local one (the client).

This package provides a range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.

Create and manage unique directories for each TensorFlow training run. This package provides a unique, time stamped directory for each run along with functions to retrieve the directory of the latest run or latest several runs.

This package provides an R interface to the Tabler HTML template. tablerDash is a light Bootstrap 4 dashboard template. There are different layouts available such as a one page dashboard or a multi-page template, where the navigation menu is contained in the navigation bar.

This is a package for visualizing functional data and identifying functional outliers with bagplots, boxplots and rainbow plots.

This package provides a fast implementation of hierarchical clustering.

This package lets you manage Google Drive files from R.

This package provides some very simple method functions for confidence interval calculation and to distill pertinent information from a potentially complex object; primarily used in common with the packages extRemes and SpatialVx.

This package provides visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. The package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015).

This package provides functions that implement the known population median test.

This package provides functions for computing the density and the distribution function of multivariate normal and "t" random variables, and for generating random vectors sampled from these distributions. Probabilities are computed via non-Monte Carlo methods.

When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions.

This package provides tools to identify and read BMP, JPEG, PNG, and TIFF format bitmap images. Identification defaults to the use of the magic number embedded in the file rather than the file extension.

This package contains functions to generate pre-defined summary statistics from activPAL events files. The package also contains functions to produce informative graphics that visualize physical activity behaviour and trends. This includes generating graphs that align physical activity behaviour with additional time based observations described by other data sets, such as sleep diaries and continuous glucose monitoring data.

This package provides a set of S3 generics to axe components of fitted model objects and help reduce the size of model objects saved to disk.

This package provides two methods of plotting categorical scatter plots such that the arrangement of points within a category reflects the density of data at that region, and avoids over-plotting.

This package provides a package for quantifying, profiling and removing cell free mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments.

R-wrs2 offers a range of strong stats methods from Wilcox WRS functions. It implements robust t-tests, both independent and dependent, robust ANOVA, including designs with between-within subjects, quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models using robust location measures.

The gdtools package provides functionalities to get font metrics and to generate base64 encoded string from raster matrix.

This package contains tools for exploring Hardy-Weinberg equilibrium for diallelic genetic marker data. All classical tests (chi-square, exact, likelihood-ratio and permutation tests) for Hardy-Weinberg equilibrium are included in the package, as well as functions for power computation and for the simulation of marker data under equilibrium and disequilibrium. Routines for dealing with markers on the X-chromosome are included. Functions for testing equilibrium in the presence of missing data by using multiple imputation are also provided. Implements several graphics for exploring the equilibrium status of a large set of diallelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots.

This package calculates classic and/or bootstrap confidence intervals for many parameters such as the population mean, variance, interquartile range (IQR), median absolute deviation (MAD), skewness, kurtosis, Cramer's V, odds ratio, R-squared, quantiles (including median), proportions, different types of correlation measures, difference in means, quantiles and medians. Many of the classic confidence intervals are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap confidence intervals are calculated with the R package boot. Both one- and two-sided intervals are supported.

Pdist computes the euclidean distance between rows of a matrix X and rows of another matrix Y. Previously, this could be done by binding the two matrices together and calling dist, but this creates unnecessary computation by computing the distances between a row of X and another row of X, and likewise for Y. Pdist strictly computes distances across the two matrices, not within the same matrix, making computations significantly faster for certain use cases.