This package provides functions for Bayesian A/B testing including prior elicitation options based on Kass and Vaidyanathan (1992) doi:10.1111/j.2517-6161.1992.tb01868.x.

Machine Learning models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance, but such black-box models usually lack interpretability. The DALEX package contains various explainers that help to understand the link between input variables and model output.

This package provides various tools for creating iterators, many patterned after functions in the Python itertools module, and others patterned after functions in the snow package.

This package provides functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. It boasts support for many additional probability distributions to model insurance loss amounts and loss frequency: 19 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. It also supports phase-type distributions commonly used to compute ruin probabilities.

This package offers quick statistical hypothesis testing for matrix rows/columns. The main goals are speed through vectorization, detailed and user-friendly output, and compatibility with tests implemented in R.

This package provides a versatile interior point solver that solves linear programs (LPs), quadratic programs (QPs), second-order cone programs (SOCPs), semidefinite programs (SDPs), and problems with exponential and power cone constraints (https://clarabel.org/stable/). For quadratic objectives, unlike interior point solvers based on the standard homogeneous self-dual embedding (HSDE) model, Clarabel handles quadratic objective without requiring any epigraphical reformulation of its objective function. It can therefore be significantly faster than other HSDE-based solvers for problems with quadratic objective functions. Infeasible problems are detected using using a homogeneous embedding technique.

Designed for simplicity, a mirai evaluates an R expression asynchronously in a parallel process, locally or distributed over the network. The result is automatically available upon completion. Modern networking and concurrency, built on nanonext and NNG (Nanomsg Next Gen), ensures reliable and efficient scheduling over fast inter-process communications or TCP/IP secured by TLS. Distributed computing can launch remote resources via SSH or cluster managers. An inherently queued architecture handles many more tasks than available processes, and requires no storage on the file system. Innovative features include support for otherwise non-exportable reference objects, event-driven promises, and asynchronous parallel map.

This package provides various methods for clustering and cluster validation. For example, it provides fixed point clustering, linear regression clustering, clustering by merging Gaussian mixture components, as well as symmetric and asymmetric discriminant projections for visualisation of the separation of groupings.

This package provides a collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.

This package performs the Baumgartner-Weiss-Schindler two-sample test of equal probability distributions (doi:10.2307/2533862). It also performs similar rank-based tests for equal probability distributions due to Neuhauser (doi:10.1080/10485250108832874) and Murakami (doi:10.1080/00949655.2010.551516).

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

This is a package for the analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported.

Read and write feather files, a lightweight binary columnar data store designed for maximum speed.

This package provides utilities for processing Rd objects and files. Extract argument descriptions and other parts of the help pages of functions.

This package provides a function to format R source code. Spaces and indent will be added to the code automatically, and comments will be preserved under certain conditions, so that R code will be more human-readable and tidy. There is also a Shiny app as a user interface in this package.

Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for analysis and visualization.

Single cell RNA sequencing datasets can be large, consisting of matrices that contain expression data for several thousand features across several thousand cells. This package is designed to easily install, manage, and learn about various single-cell datasets, provided Seurat objects and distributed as independent packages.

This package is interface to NLopt, a library for nonlinear optimization. NLopt is a library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms.

This is a package to simplify loading of system fonts and Google Fonts into R, in order to support other packages.

This package provides data structures and basic operations for ordinary sets, generalizations such as fuzzy sets, multisets, and fuzzy multisets, customizable sets, and intervals.

This package provides methods for cluster analysis. It is a much extended version of the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".

This package lets you build complex plots, heatmaps in particular, using natural semantics. Bigger plots can be assembled using directives such as LeftOf, RightOf, TopOf, and Beneath and more. Other features include clustering, dendrograms and integration with ggplot2 generated grid objects. This package is particularly designed for bioinformaticians to assemble complex plots for publication.

This package provides the functionality to set configuration options on a per-package basis. Options set by a given package only apply to that package, other packages are unaffected.

This is a package for graphical and statistical analyses of environmental data, with a focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. It provides major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. It comes with numerous built-in data sets from regulatory guidance documents and environmental statistics literature. It includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, https://link.springer.com/book/10.1007/978-1-4614-8456-1).