This package provides support for linear order and unimodal order (univariate) isotonic regression and bivariate isotonic regression with linear order on both variables.

As a successor of the packages BatchJobs and BatchExperiments, this package provides a parallel implementation of the Map function for high performance computing systems managed by various schedulers. A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way.

This package provides an R tool for estimating and partitioning R2 in generalized linear mixed models (GLMMs) based on predictor variance.

Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such experiments require specialized tools for primary processing such as GRAND-SLAM, and specialized tools for downstream analyses. grandR provides a comprehensive toolbox for quality control, kinetic modeling, differential gene expression analysis and visualization of such data.

This package provides useful tools for structural equation modeling.

The brew package implements a templating framework for mixing text and R code for report generation. The template syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module.

This package provides various methods for MRI tissue classification.

This package helps you create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.

This package provides routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.

This package provides tools and functions for parsing, rendering and operating on semantic version strings. Semantic versioning is a simple set of rules and requirements that dictate how version numbers are assigned and incremented as outlined at http://semver.org.

This package provides utilities for processing and analyzing the files that are exported from a recorded Zoom meeting. This includes analyzing data captured through video cameras and microphones, the text-based chat, and meta-data. You can analyze aspects of the conversation among meeting participants and their emotional expressions throughout the meeting.

This package estimates the parameters in Dirichlet-Multinomial and computes log-likelihoods.

This package provides common base and stats methods for rle objects, aiming to make it possible to treat them transparently as vectors.

This package provides tools for depth functions methodology applied to multivariate analysis. Besides allowing calculation of depth values and depth-based location estimators, the package includes functions or drawing contour plots and perspective plots of depth functions. Euclidean and spherical depths are supported.

Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score---these are popular metrics for assessing performance of binary classifiers for certain thresholds. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. The ROCit package provides flexibility to easily evaluate threshold-bound metrics.

This package provides a collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values.

This package implements the Figueiredo machine learning algorithm for adaptive sparsity and the Wong algorithm for adaptively sparse Gaussian geometric models.

This package computes model and semi partial R squared with confidence limits for the linear and generalized linear mixed model (LMM and GLMM). The R squared measure from L. J. Edwards et al. (2008) is extended to the GLMM using penalized quasi-likelihood (PQL) estimation (see Jaeger et al. (2016)).

This package provides ten distributions supplementing those built into R. Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi squared, Spearman's rho, maximum F ratio, the Pearson product moment correlation coefficient, Johnson distributions, normal scores and generalized hypergeometric distributions. In addition two random number generators of George Marsaglia are included.

This package provides functions for assessing the replication/preservation of a network module's topology across datasets through permutation testing.

This R package provides access to the code and data sets published by the statistics blog FiveThirtyEight.

The r-abhgenotyper package provides simple imputation, error-correction and plotting capacities for genotype data. The package is supposed to serve as an intermediate but independent analysis tool between the TASSEL GBS pipeline and the r-qtl package. It provides functionalities not found in either TASSEL or r-qtl in addition to visualization of genotypes as "graphical genotypes".

This package provides a consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.

Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for analysis and visualization.