This package provides portable tools to run system processes in the background. It can check if a background process is running; wait on a background process to finish; get the exit status of finished processes; kill background processes and their children; restart processes. It can read the standard output and error of the processes, using non-blocking connections. processx can poll a process for standard output or error, with a timeout. It can also poll several processes at once.

This is a dedicated package to WELL pseudo random generators, which were introduced in Panneton et al. (2006), ``Improved Long-Period Generators Based on Linear Recurrences Modulo 2'', ACM Transactions on Mathematical Software.

This package provides functionality to dynamically define R functions and S4 methods with inlined C, C++ or Fortran code supporting .C and .Call calling conventions.

Calculate generalized R-squared, partial R-squared, and partial correlation coefficients for generalized linear (mixed) models (including quasi models with well defined variance functions).

This package provides cover-tree and kd-tree fast k-nearest neighbor search algorithms. Related applications including KNN classification, regression and information measures are implemented.

This package provides key-value stores with automatic pruning. Caches can limit either their total size or the age of the oldest object (or both), automatically pruning objects to maintain the constraints.

This package provides an interface to the NetCDF file formats designed by Unidata for efficient storage of array-oriented scientific data and descriptions. Most capabilities of NetCDF version 4 are supported. Optional conversions of time units are enabled by UDUNITS version 2, also from Unidata.

The grammar of graphics as shown in ggplot2 has provided an expressive API for users to build plots. This package ggside extends ggplot2 by allowing users to add graphical information about one of the main panel's axis using a familiar ggplot2 style API with tidy data. This package is particularly useful for visualizing metadata on a discrete axis, or summary graphics on a continuous axis such as a boxplot or a density distribution.

This package provides kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods kernlab includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.

This package implements the Subplex optimization algorithm. It solves unconstrained optimization problems using a simplex method on subspaces. The method is well suited for optimizing objective functions that are noisy or are discontinuous at the solution.

This package guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems.

This package provides high level functions for parallel programming with Rcpp. For example, the parallelFor() function can be used to convert the work of a standard serial for loop into a parallel one and the parallelReduce() function can be used for accumulating aggregates or other values.

With this package you can add in-app user authentication to Shiny, allowing you to secure publicly hosted apps and build dynamic user interfaces from user information.

This package provides tools for the variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection based on the importance spectrum (somewhat similar to scree plots; for the selection of large, potentially highly-correlated variables). The main applications are in high-dimensional data (e.g., microarray data, and other genomics and proteomics applications).

This package provides qualitatively constrained (regression) smoothing splines via linear programming and sparse matrices.

This package provides an interface for working with large matrices stored in files, not in computer memory. It supports multiple non-character data types (double, integer, logical and raw) of various sizes (e.g. 8 and 4 byte real values). Access to parts of the matrix is done by indexing, exactly as with usual R matrices. It supports very large matrices; the package has been tested on multi-terabyte matrices. It allows for more than 2^32 rows or columns, ad allows for quick addition of extra columns to a filematrix.

This package implements faster versions of base R functions (e.g. mean, standard deviation, covariance, weighted mean), mostly written in C++, along with miscellaneous functions for various purposes (e.g. create the histogram with fitted probability density function or probability mass function curve, create the body mass index groups, assess the linearity assumption in logistic regression).

This package enables conversions between R objects and JavaScript Object Notation (JSON) using the rapidjsonr library.

This package Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests. Also now includes some software for quickly recoding survey data and plotting point estimates from interaction terms in regressions (and multiply imputed regressions). NOTE: Weighted partial correlation calculations pulled to address a bug.

This package offers a set of functions for extending dendrogram objects in R, letting you visualize and compare trees of hierarchical clusterings. You can adjust a tree's graphical parameters (the color, size, type, etc of its branches, nodes and labels) and visually and statistically compare different dendrograms to one another.

This package makes the qhull library available in R, in a similar manner as in Octave. Qhull computes convex hulls, Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams. It runs in 2-d, 3-d, 4-d, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull does not support constrained Delaunay triangulations, or mesh generation of non-convex objects, but the package does include some R functions that allow for this. Currently the package only gives access to Delaunay triangulation and convex hull computation.

This package provides tools to compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, Steel's multiple comparison test, and the Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie pattern. Except for Steel's test and the JT test it also combines these tests across several blocks of samples.

This package estimates conditional Akaike information in mixed-effect models. These models are fitted using (g)lmer() from lme4, lme() from nlme, and gamm() from mgcv. The provided functions facilitate the computation of the conditional Akaike information for model evaluation.

This package is a port of the gWidgets2 API for the tcltk package.