Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.

The RDieHarder package provides an R interface to the DieHarder suite of random number generators and tests that was developed by Robert G. Brown and David Bauer, extending earlier work by George Marsaglia and others. The DieHarder library code is included.

Minimally adjust the values of numerical records in a data.frame, such that each record satisfies a predefined set of equality and/or inequality constraints. The constraints can be defined using the validate package. The core algorithms have recently been moved to the lintools package, refer to lintools for a more basic interface and access to a version of the algorithm that works with sparse matrices.

Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) <https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.

Makes it easy to produce everyday ggplot2 charts in a functional way without an extensive "tree" implementation. The package includes over 15 functions for the production and arrangement of basic graphing.

This package contains a function to randomize subjects, patients in groups of sequences (treatment sequences). If a blocksize is given, the randomization will be done within blocks. The randomization may be controlled by a Wald-Wolfowitz runs test. Functions to obtain the p-value of that test are included. The package is mainly intended for randomization of bioequivalence studies but may be used also for other clinical crossover studies. Contains two helper functions sequences() and williams() to get the sequences of commonly used designs in BE studies.

Make your workflow faster and easier. Easily customizable plots (via ggplot2'), nice APA tables (following the style of the *American Psychological Association*) exportable to Word (via flextable'), easily run statistical tests or check assumptions, and automatize various other tasks.

This package provides functions to implement the parametric and non-parametric bootstrap confidence interval methods described in Morrison and Simon (2017) <arXiv:1702.06986>.

Enhances the R Optimization Infrastructure ('ROI') package with the quadratic solver OSQP'. More information about OSQP can be found at <https://osqp.org>.

This package contains functions useful for reading in Licor 6800 files, correcting and analyzing rapid A/Ci response (RACiR) data. Requires some user interaction to adjust the calibration (empty chamber) data file to a useable range. Calibration uses a 1st to 5th order polynomial as suggested in Stinziano et al. (2017) <doi:10.1111/pce.12911>. Data can be processed individually or batch processed for all files paired with a given calibration file. RACiR is a trademark of LI-COR Biosciences, and used with permission.

The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) <doi:10.1007/BF00045728>. The implementation in this package is fast and flexible: it allows for the use of any (environmental) predictor variable. Predictors can be either static (for example, soil pH) or dynamic (for example, monthly precipitation).

This package provides a cross-validated minimal-optimal feature selection algorithm. It utilises popularity counting, hierarchical clustering with feature dissimilarity measures, and prefiltering with all-relevant feature selection method to obtain the minimal-optimal set of features.

This package provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.

Wrapper for the RSpace Electronic Lab Notebook (<https://www.researchspace.com/>) API. This packages provides convenience functions to browse, search, create, and edit your RSpace documents. In addition, it enables filling RSpace templates from R Markdown/Quarto templates or tabular data (e.g., Excel files). This R package is not developed or endorsed by Research Space'.

It computes the Schmidt decomposition of bipartite quantum systems, discrete or continuous, and their respective entanglement metrics. See Artur Ekert, Peter L. Knight (1995) <doi:10.1119/1.17904> for more details.

Compress local and online images using the reSmush.it API service <https://resmush.it/>.

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>.

This package provides a collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM.

This package implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.

This package implements Bayesian inference for the conditional genetic stock identification model. It allows inference of mixed fisheries and also simulation of mixtures to predict accuracy. A full description of the underlying methods is available in a recently published article in the Canadian Journal of Fisheries and Aquatic Sciences: <doi:10.1139/cjfas-2018-0016>.

Provide color schemes for maps (and other graphics) based on the color palettes of several Microsoft(r) products. Forked from RColorBrewer v1.1-2.

An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for 210Pb-dated deposits using prior information. It can combine 210Pb, radiocarbon, and other dates in the chronologies. See Aquino et al. (2018) <doi:10.1007/s13253-018-0328-7>. Note that parts of the code underlying rplum are derived from the rbacon package by the same authors, and there remains a degree of overlap between the two packages.

It is a package that provides alternative approach for finding optimum parameters of ridge regression. This package focuses on finding the ridge parameter value k which makes the variance inflation factors closest to 1, while keeping them above 1 as addressed by Michael Kutner, Christopher Nachtsheim, John Neter, William Li (2004, ISBN:978-0073108742). Moreover, the package offers end-to-end functionality to find optimum k value and presents the detailed ridge regression results. Finally it shows three sets of graphs consisting k versus variance inflation factors, regression coefficients and standard errors of them.

This package provides functions to compute the modularity and modularity-related roles in networks. It is a wrapper around the rgraph library (Guimera & Amaral, 2005, <doi:10.1038/nature03288>).