This package provides Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.

This package provides tools for measuring inequality, concentration, and poverty measures. It provides both empirical and theoretical Lorenz curves.

This package provides an interface to the C implementation of the random number generator with multiple independent streams developed by L'Ecuyer et al (2002). The main purpose of this package is to enable the use of this random number generator in parallel R applications.

This package lets you compute the median ranking according to Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete. The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. The package also provides some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient.

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 is a C/C++ based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R. It further includes fast functions for common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data.

This package provides visualizations for SHAP (SHapley Additive exPlanations) such as waterfall plots, force plots, various types of importance plots, dependence plots, and interaction plots. These plots act on a shapviz object created from a matrix of SHAP values and a corresponding feature dataset. Wrappers for the R packages xgboost, lightgbm, fastshap, shapr, h2o, treeshap, DALEX, and kernelshap are added for convenience. By separating visualization and computation, it is possible to display factor variables in graphs, even if the SHAP values are calculated by a model that requires numerical features. The plots are inspired by those provided by the shap package in Python, but there is no dependency on it.

This package provides tools to help working with text files. It can return the number of lines; print the first and last lines; convert encoding. Operations are made without reading the entire file before starting, resulting in good performances with large files.

Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models. The package includes demos reproducing analyzes presented in the book "Multiple Comparisons Using R" (Bretz, Hothorn, Westfall, 2010, CRC Press).

This package provides exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis.

This package provides functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.)

This is a framework for fitting multiple caret models. It uses the same re-sampling strategy as well as creating ensembles of such models. Use caretList to fit multiple models and then use caretEnsemble to combine them greedily or caretStack to combine them using a caret model.

This package contains functions for non-parametric survival analysis of exact and interval-censored observations.

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

This package provides a framework for text mining applications within R.

The r-abd package contains data sets and sample code for the Analysis of biological data by Michael Whitlock and Dolph Schluter.

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 OrgMassSpecR is an extension of the R statistical computing language. It contains functions to assist with organic or biological mass spectrometry data analysis. Mass spectral libraries are available as companion packages.

This package implements easy-to-use functions to generate 2-7 sets Venn plot in publication quality. ggVennDiagram plot Venn using well-defined geometry dataset and ggplot2. The shapes of 2-4 sets Venn use circles and ellipses, while the shapes of 4-7 sets Venn use irregular polygons (4 has both forms), which are developed and imported from another package venn. We provide internal functions to integrate shape data with user provided sets data, and calculated the geometry of every regions/intersections of them, then separately plot Venn in three components: set edges, set labels, and regions. From version 1.0, it is possible to customize these components as you demand in ordinary ggplot2 grammar.

This package converts back and forth between two representations of a convex polytope: as solution of a set of linear equalities and inequalities and as convex hull of set of points and rays. Also does linear programming and redundant generator elimination. All functions can use exact infinite-precision rational arithmetic.

This package helps you to automate R package and project setup tasks that are otherwise performed manually. This includes setting up unit testing, test coverage, continuous integration, Git, GitHub integration, licenses, Rcpp, RStudio projects, and more.

This package exposes R bindings to jsTree, a JavaScript library that supports interactive trees, to enable rich, editable trees in Shiny.

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.

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