This package provides an improved heatmap package. It is completely compatible with the original R function heatmap, and provides more powerful and convenient features.

This package implements the fast cross-validation via sequential testing (CVST) procedure. CVST is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.

This package implements many algorithms for statistical learning on sparse matrices: matrix factorizations, matrix completion, elastic net regressions, factorization machines. The rsparse package also enhances the Matrix package by providing methods for multithreaded <sparse, dense> matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format.

Dunn's test computes stochastic dominance & reports pairwise comparisons. This is done following a Kruskal-Wallis test (Kruskal and Wallis, 1952). It employs Dunn's z-test-statistic approximations for rank statistics, conducting k(k-1)/2 comparisons. The null hypothesis assumes that the probability of a randomly selected value from the first group being larger than one from the second group is one half, similar to the Wilcoxon-Mann-Whitney test. Dunn's test serves as a test for median difference and takes into account tied ranks.

The ROI is a framework for handling optimization problems in R.

This package provides procedures to work with classification and regression trees.

This package implements the regularized Gaussian maximum likelihood estimation of the inverse of a covariance matrix. It uses Newton's method and coordinate descent to solve the regularized inverse covariance matrix estimation problem.

Magrittr provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions. For more information, see package vignette. To quote Rene Magritte, "Ceci n'est pas un pipe."

Circular layout is an efficient way to visualise huge amounts of information. This package provides an implementation of circular layout generation in R as well as an enhancement of available software. Its flexibility is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, it gives users more convenience and freedom to design figures for better understanding complex patterns behind multi-dimensional data.

This package computes alpha and beta diversity metrics using concurrent C threads. Metrics include UniFrac, Faith's phylogenetic diversity, Bray-Curtis dissimilarity, Shannon diversity index, and many others. Also parses newick trees into phylo objects and rarefies feature tables.

Currently there are many functions in S-PLUS that are missing in R. To facilitate the conversion of S-PLUS packages to R packages, this package provides some missing S-PLUS functionality in R.

This is a package for stubbing and setting expectations on HTTP requests. It includes tools for stubbing HTTP requests, including expected request conditions and response conditions. You can match on HTTP method, query parameters, request body, headers and more. It can be used for unit tests or outside of a testing context.

This package provides tools to infer the code style (which style rules are followed and which ones are not) from one package and use it to check another. This makes it easier to find and correct the most important problems first.

This package provides a differential evolution (DE) stochastic algorithms for global optimization of problems with and without constraints. The aim is to curate a collection of its state-of-the-art variants that

1. do not sacrifice simplicity of design,

2. are essentially tuning-free, and

3. can be efficiently implemented directly in the R language.

This package does local optimization using two derivatives and trust regions. Guaranteed to converge to local minimum of objective function.

This package provides a %dopar% adapter such that any type of futures can be used as backends for the foreach framework.

This package provides tools to compute marginal effects from statistical models and return the result as tidy data frames. These data frames are ready to use with the ggplot2 package. Marginal effects can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The two main functions are ggpredict() and ggeffect(). There is a generic plot() method to plot the results using ggplot2.

This package provides several analysis-related functions for the book entitled "R statistics and graph for medical articles" (written in Korean), version 1, by Keon-Woong Moon with Korean demographic data with several plot functions.

This package contains functionality for performing the following methods of p-value aggregation: Fisher's method, the Lancaster method (weighted Fisher's method), and Sidak correction.

Suppose we have data that has so many series that it is hard to identify them by their colors as the differences are so subtle. With gghighlight we can highlight those lines that match certain criteria. The result is a usual ggplot object, so it is fully customizable and can be used with custom themes and facets.

This package provides a collection of methods for smoothing numerical data, commencing with a port of the Matlab gaussian window smoothing function. In addition, several functions typically used in smoothing of financial data are included.

Postprocessors refine predictions outputted from machine learning models to improve predictive performance or better satisfy distributional limitations. This package introduces tailor objects, which compose iterative adjustments to model predictions. A number of pre-written adjustments are provided with the package, such as calibration. See Lichtenstein, Fischhoff, and Phillips (1977) <doi:10.1007/978-94-010-1276-8_19>. Other methods and utilities to compose new adjustments are also included. Tailors are tightly integrated with the tidymodels framework.

This package provides functions and scripts used in the book "Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences" by Ron Wehrens, Springer (2011).

This package provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.