This package provides the means to plot ggplot2 graphs in the style of the XKCD web comic.

This package provides alternative statistical methods for meta-analysis, including:

1. bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences

2. heterogeneity tests and measures that are robust to outliers;

3. measures, tests, and visualization tools for publication bias or small-study effects;

4. meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc.;

5. meta-analysis methods for synthesizing proportions;

6. models for multivariate meta-analysis.

This package provides functionality to benchmark your CPU and compare against other CPUs. Also provides functions for obtaining system specifications, such as RAM, CPU type, and R version.

This package provides the datasets to support the Fish Stock Assessment (FSA) package.

Run R CMD check from R programmatically, and capture the results of the individual checks.

This package provides a collection of high-performance utilities. It can be used to compute distances, correlations, autocorrelations, clustering, and other tasks. It also contains a graph clustering algorithm described in MetaCell analysis of single-cell RNA-seq data using K-nn graph partitions.

This package implements generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.

This package provides an alternative to R's built-in functionality for handling regular expressions, based on the Onigmo library. It offers first-class compiled regex objects, partial matching and function-based substitutions, amongst other features.

A workflow is an object that can bundle together your pre-processing, modeling, and post-processing requests. For example, if you have a recipe and parsnip model, these can be combined into a workflow. The advantages are:

1. You don’t have to keep track of separate objects in your workspace.

2. The recipe prepping and model fitting can be executed using a single call to fit().

3. If you have custom tuning parameter settings, these can be defined using a simpler interface when combined with tune.

4. In the future, workflows will be able to add post-processing operations, such as modifying the probability cutoff for two-class models.

This package provides statistical models of biased sampling in the form of univariate and multivariate noncentral hypergeometric distributions, including Wallenius' noncentral hypergeometric distribution and Fisher's noncentral hypergeometric distribution (also called extended hypergeometric distribution).

Lp_solve is software for solving linear, integer and mixed integer programs. This implementation supplies a "wrapper" function in C and some R functions that solve general linear/integer problems, assignment problems, and transportation problems.

This package provides a complete environment for Bayesian inference using a variety of different samplers.

This is an R package for the imputation of left-censored data under a compositional approach. The implemented methods consider aspects of relevance for a compositional approach such as scale invariance, subcompositional coherence or preserving the multivariate relative structure of the data. Based on solid statistical frameworks, it comprises the ability to deal with single and varying censoring thresholds, consistent treatment of closed and non-closed data, exploratory tools, multiple imputation, Markov Chain Monte Carlo (MCMC), robust and non-parametric alternatives, and recent proposals for count data.

Analyze count time series with excess zeros. Two types of statistical models are supported: Markov regression and state-space models. They are also known as observation-driven and parameter-driven models respectively in the time series literature. The functions used for Markov regression or observation-driven models can also be used to fit ordinary regression models with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) assumption. The package also contains miscellaneous functions to compute density, distribution, quantile, and generate random numbers from ZIP and ZINB distributions.

This package provides software to accompany the book "Wavelet Methods for Time Series Analysis", Donald B. Percival and Andrew T. Walden, Cambridge University Press, 2000.

This package provides interfaces to audio devices (mainly sample-based) from R to allow recording and playback of audio.

The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds. The implementation can be used via nls-like calls using the nlsLM function.

The base functions for set operations in R can be used for only two sets. This package RVenn provides functions for dealing with multiple sets. It uses purr to find the union, intersection and difference of three or more sets. This package also provides functions for pairwise set operations among several sets. Further, based on ggplot2 and ggforce, a Venn diagram can be drawn for two or three sets. For bigger data sets, a clustered heatmap showing the presence or absence of the elements of the sets can be drawn based on the pheatmap package. Finally, enrichment test can be applied to two sets whether an overlap is statistically significant or not.

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 provides convenience functions for analyzing factorial experiments using ANOVA or mixed models.

The main function archetypes implements a framework for archetypal analysis supporting arbitrary problem solving mechanisms for the different conceptual parts of the algorithm.

This package provides the usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models. Also, there are provided kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the evd package is provided, so that users can safely interchange most code.

This package lets you use multiple fill and color scales in ggplot2.

This package provides functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level.