This package provides fast algorithms for the Theil-Sen estimator, Siegel's repeated median slope estimator, and Passing-Bablok regression. The implementation is based on algorithms by Dillencourt et al. (1992) <doi:10.1142/S0218195992000020> and Matousek et al. (1998) <doi:10.1007/PL00009190>. The implementations are detailed in Raymaekers (2023) <doi:10.32614/RJ-2023-012> and Raymaekers J., Dufey F. (2022) <arXiv:2202.08060>. All algorithms run in quasilinear time.

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 software for the book Spectral Analysis for Physical Applications, Donald B. Percival and Andrew T. Walden, Cambridge University Press, 1993.

Multivariate data sets often differ in several factors or derived statistical parameters, which have to be selected for a valid interpretation. Basing this selection on traditional statistical limits leads occasionally to the perception of losing information from a data set. This package provides tools to calculate these limits on the basis of the mathematical properties of the distribution of the analyzed items.

This package implements the RUV (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are RUV-2, RUV-4, RUV-inv, RUV-rinv, RUV-I, and RUV-III, along with various supporting algorithms.

The purpose of this package is to factor out logic and math common to Item Factor Analysis fitting, diagnostics, and analysis. It is envisioned as core support code suitable for more specialized IRT packages to build upon. Complete access to optimized C functions is made available with R_RegisterCCallable().

The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".

The futile.options subsystem provides an easy user-defined options management system that is properly scoped. This means that options created via futile.options are fully self-contained and will not collide with options defined in other packages.

This package provides functions for working with the scrypt key derivation functions. Scrypt is a password-based key derivation function created by Colin Percival. The algorithm was specifically designed to make it costly to perform large-scale custom hardware attacks by requiring large amounts of memory.

This package performs estimation of physical activity and sedentary behavior variables from activPAL events files.

This package provides an implementation of scatter plots for plotting. a three dimensional point cloud.

This package provides functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.

This package provides tooling to group dates by a variety of periods including: yearly, monthly, by second, by week of the month, and more. The groups are defined in such a way that they also represent the distance between dates in terms of the period. This extracts valuable information that can be used in further calculations that rely on a specific temporal spacing between observations.

This package implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done.

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.

This package lets you fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models.

This package provides functions for regulation, decomposition and analysis of space-time series. The pastecs library is a PNEC-Art4 and IFREMER initiative to bring PASSTEC 2000 functionalities to R.

This package provides a set of tools for the statistical analysis of data using:

1. normal linear models;

2. generalized linear models;

3. negative binomial regression models as alternative to the Poisson regression models under the presence of overdispersion;

4. beta-binomial and random-clumped binomial regression models as alternative to the binomial regression models under the presence of overdispersion;

5. zero-inflated and zero-altered regression models to deal with zero-excess in count data;

6. generalized nonlinear models;

7. generalized estimating equations for cluster correlated data.

This package provides functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).

This package contains the data set for the crowd-sourced benchmarks from running the benchmarkme package.

This package provides tools for Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).

This package provides tools to render DOT diagram markup language in R and also provides the possibility to export the graphs in PostScript and SVG (Scalable Vector Graphics) formats. In addition, it supports literate programming packages such as knitr and rmarkdown.

This package provides tools to identify global ("unknown" or "free") objects in R expressions by code inspection using various strategies, e.g. conservative or liberal. The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in distributed compute environments.

This package defines sparse three-dimensional arrays and supports standard operations on them. The package also includes utility functions for matrix calculations that are common in statistics, such as quadratic forms.