Outliers virtually exist in any datasets of any application field. To avoid the impact of outliers, we need to use robust estimators. Classical estimators of multivariate mean and covariance matrix are the sample mean and the sample covariance matrix. Outliers will affect the sample mean and the sample covariance matrix, and thus they will affect the classical factor analysis which depends on the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser, P. and Croux, C. (2003) <doi:10.1016/S0047-259X(02)00007-6>). So it is necessary to use the robust estimators of the sample mean and the sample covariance matrix. There are several robust estimators in the literature: Minimum Covariance Determinant estimator, Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid, M, S, and Stahel-Donoho. The most direct way to make multivariate analysis more robust is to replace the sample mean and the sample covariance matrix of the classical estimators to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006) <doi:10.1002/0470010940>) (Todorov, V. and Filzmoser, P. (2009) <doi:10.18637/jss.v032.i03>), which is our choice of robust factor analysis. We created an object oriented solution for robust factor analysis based on new S4 classes.

This package provides functions for implementing robust methods for functional linear regression. In the functional linear regression, we consider scalar-on-function linear regression and function-on-function linear regression.

This package provides environment modules functionality, which enables use of the Environment Modules system (<http://modules.sourceforge.net/>) from within the R environment. By default the user's login shell environment (ie. "bash -l") will be used to initialize the current session. The module function can also; load or unload specific software, list all the loaded software within the current session, and list all the applications available for loading from the module system. Lastly, the module function can remove all loaded software from the current session.

Three-step regression and inference for cellwise and casewise contamination.

This package provides a data structure and toolkit for documenting and recoding categorical data that can be shared in other statistical software.

This package provides clean, tidy access to climate and weather data from the National Oceanic and Atmospheric Administration ('NOAA') via the National Centers for Environmental Information ('NCEI') Data Service API <https://www.ncei.noaa.gov/access/services/data/v1>. Covers daily weather observations, monthly and annual summaries, and 30-year climate normals from over 100,000 stations across 180 countries. No API key is required. Dedicated functions handle the most common datasets, while a generic fetcher provides access to all NCEI datasets. Station discovery functions help users find stations by location or name. Data is downloaded on first use and cached locally for subsequent calls. This package is not endorsed or certified by NOAA'.

Gene-environment (GÃ E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of GÃ E studies have been commonly encountered, leading to the development of a broad spectrum of robust penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian variable selection method for GÃ E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.

An R interface to the SYMPHONY solver for mixed-integer linear programs.

Computes the power resulting from completely randomized and rerandomized experiments with two groups. Furthermore, computes the sample size necessary to obtain a desired level of power for completely randomized and rerandomized experiments.

Visualize networks using the javascript library roughjs'. This allows to draw sketchy, hand-drawn-like networks.

Native R interface to TMB (Template Model Builder) so models can be written entirely in R rather than C++'. Automatic differentiation, to any order, is available for a rich subset of R features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. RTMB provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.

Presentation-ready results tables for epidemiologists in an automated, reproducible fashion. The user provides the final analytical dataset and specifies the design of the table, with rows and/or columns defined by exposure(s), effect modifier(s), and estimands as desired, allowing to show descriptors and inferential estimates in one table -- bridging the rift between epidemiologists and their data, one table at a time. See Rothman (2017) <doi:10.1007/s10654-017-0314-3>.

Run simple R scripts as command line applications, with automatic robust and convenient support for command line arguments. This package provides Rapp', an alternative R front-end similar to Rscript', that enables this.

Statistical tools for the Mallows-Binomial model, the first joint statistical model for preference learning for rankings and ratings. This project was supported by the National Science Foundation under Grant No. 2019901.

This package provides functions used in the R: Einführung durch angewandte Statistik (second edition).

This package provides a series of functions in some way considered useful to the author. These include methods for subsetting tables and generating indices for arrays, conditioning and intervening in probability distributions, generating combinations, fast transformations, and more...

This package provides algorithms to locate multiple distributional change-points in piecewise stationary time series. The algorithms are provably consistent, even in the presence of long-range dependencies. Knowledge of the number of change-points is not required. The code is written in Go and interfaced with R.

Estimates the rank intraclass correlation coefficient (ICC) for clustered continuous and ordinal data. See Tu et al. (2023) <DOI:10.1002/sim.9864> for details.

Enables the use of color palettes inspired by the Dune movies. These palettes are compatible with ggplot2'. See Wickham (2016) <doi:10.1007/978-3-319-24277-4> for more details on ggplot2'.

This package provides methods readMat() and writeMat() for reading and writing MAT files. For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.

Set of analytical procedures based on advanced data analysis in support of Brazil's public sector external control activity.

Automated performance of common transformations used to fulfill parametric assumptions of normality and identification of the best performing method for the user. Output for various normality tests (Thode, 2002) corresponding to the best performing method and a descriptive statistical report of the input data in its original units (5-number summary and mathematical moments) are also presented. Lastly, the Rankit, an empirical normal quantile transformation (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard use cases and facilitate adoption. <DOI: 10.1201/9780203910894>. <DOI: 10.22237/jmasm/1257034080>.

This package provides a full-featured RethinkDB <https://rethinkdb.com/> client for R; allows users to store JSON-serialised data in a robust, distributed no-SQL system, use rich queries and react to data changes in real-time.

This package provides estimation and data generation tools for several new regression models, including the gamma, beta, inverse gamma and beta prime distributions. These models can be parameterized based on the mean, median, mode, geometric mean and harmonic mean, as specified by the user. For details, see Bourguignon and Gallardo (2025a) <doi:10.1016/j.chemolab.2025.105382> and Bourguignon and Gallardo (2025b) <doi:10.1111/stan.70007>.