This package provides a color picker that can be used as an input in Shiny apps or Rmarkdown documents. The color picker supports alpha opacity, custom color palettes, and many more options. A plot color helper tool is available as an RStudio Addin, which helps you pick colors to use in your plots. A more generic color picker RStudio Addin is also provided to let you select colors to use in your R code.

This package provides resampling procedures to assess the stability of selected variables with additional finite sample error control for high-dimensional variable selection procedures such as Lasso or boosting. Both, standard stability selection (Meinshausen & Buhlmann, 2010) and complementary pairs stability selection with improved error bounds (Shah & Samworth, 2013) are implemented. The package can be combined with arbitrary user specified variable selection approaches.

This package provides an R interface to the GNU Linear Programming Kit, software for solving large-scale linear programming (LP), mixed integer linear programming (MILP) and other related problems.

This package provides a parallel backend for the %dopar% function using the multicore functionality of the parallel package.

This package provides some functions for sample classification in microarrays.

This package provides an implementation of efficient approximate leave-one-out (LOO) cross-validation for Bayesian models fit using Markov chain Monte Carlo, as described in doi:10.1007/s11222-016-9696-4. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.

This package provides a set of Shiny apps for effective communication and understanding in statistics. The current version includes properties of normal distribution, properties of sampling distribution, one-sample z and t tests, two samples independent (unpaired) t test and analysis of variance.

This package provides a quantitative financial modelling framework to allow users to specify, build, trade, and analyse quantitative financial trading strategies.

This package lets you fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models.

This package provides several methods for performing permutation tests. It has three main functions, to perform linear permutation tests. These tests are tests where the test statistic is the sum of the product of a covariate (usually group indicator) and the scores.

Rasterize only specific layers of a ggplot2 plot while simultaneously keeping all labels and text in vector format. This allows users to keep plots within the reasonable size limit without losing vector properties of the scale-sensitive information.

This package offers an interactive function for the detection of breakpoints in series.

This package extends the ggplot2 plotting system to support network visualization. Inspired by ggtree, ggtangle is designed to work with network associated data.

This package provides an R interface to Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen and Guestrin (2016). The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.

This package is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates. Over years, the package has been developed in-between many projects hence also in line with the name (gap).

This is a package for the manipulation of genetic data (SNPs). Computation of genetic relationship matrix (GRM) and dominance matrix, linkage disequilibrium (LD), and heritability with efficient algorithms for linear mixed models (AIREML).

This package provides a re-implementation of the gWidgets API. The API is defined in this package. A second, toolkit-specific package is required to use it.

This package provides a fast dimensionality reduction method scalable to large numbers of samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS, but rather than calculating a complete distance matrix between all pairs of samples, only the distances between a set of landmarks and the samples are calculated.

This package performs search for the global minimum of a very complex non-linear objective function with a very large number of optima.

This package provides an R interface to HiGHS, an optimization solver. It is designed for solving mixed-integer optimization problems with quadratic or linear objectives and linear constraints.

This package is for genomic regions processing using command line tools such as BEDTools, BEDOPS and Tabix. These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging. The bedr package's API enhances access to these tools as well as offers additional utilities for genomic regions processing.

This package provides methods for spatial data analysis, especially raster data. The included methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction. Processing of very large files is supported.

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.

The aim of SHAPforxgboost is to aid in visual data investigations using SHAP (Shapley additive explanation) visualization plots for XGBoost. It provides summary plot, dependence plot, interaction plot, and force plot. It relies on the XGBoost package to produce SHAP values.