Robust categorical data analysis based on the theory of C-estimation developed in Welz (2024) <doi:10.48550/arXiv.2403.11954>. For now, the package only implements robust estimation of polychoric correlation as proposed in Welz, Mair and Alfons (2026) <doi:10.1017/psy.2025.10066> and robust estimation of polyserial correlation (Welz, 2026 <doi:10.1017/psy.2026.10091>) with methods for printing and plotting. We will implement further models in future releases. In addition, the package is still experimental, so input arguments and class structure may change in future releases.

R Commander plug-in for repeated-measures and mixed-design ('split-plot') ANOVA. It adds a new menu entry for repeated measures that allows to deal with up to three within-subject factors and optionally with one or several between-subject factors. It also provides supplementary options to oneWayAnova() and multiWayAnova() functions, such as choice of ANOVA type, display of effect sizes and post hoc analysis for multiWayAnova().

This package contains various tools to perform and visualize Response Item Networks ('ResIN's'). ResIN dummy-codes ordered and qualitative response choices from (survey) data, calculates pairwise associations and maps the location of each item response as a node in a force-directed network. Please refer to <https://www.resinmethod.net/> for more details.

This package provides 3D plotting routines that facilitate the use of the rgl package and extend its functionality. For example, the routines allow the user to directly control the camera position & orientation, as well as to generate 3D movies with a moving observer.

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 provides functions to allow users to build and analyze design consistent tree and random forest models using survey data from a complex sample design. The tree model algorithm can fit a linear model to survey data in each node obtained by recursively partitioning the data. The splitting variables and selected splits are obtained using a randomized permutation test procedure which adjusted for complex sample design features used to obtain the data. Likewise the model fitting algorithm produces design-consistent coefficients to any specified least squares linear model between the dependent and independent variables used in the end nodes. The main functions return the resulting binary tree or random forest as an object of "rpms" or "rpms_forest" type. The package also provides methods modeling a "boosted" tree or forest model and a tree model for zero-inflated data as well as a number of functions and methods available for use with these object types.

Implementation of Gibbs sampling algorithm for Bayesian Estimation of the Reduced Reparameterized Unified Model ('rrum'), described by Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.

This package provides a bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in Song, Langfelder and Horvath (2013) <doi:10.1186/1471-2105-14-5>.

This package provides randomization tests and graphical diagnostics for assessing randomized assignment and covariate balance for a binary treatment variable. See Branson (2021) <arXiv:1804.08760> for details.

Time the execution of overlapping or unique Rcpp code chunks using convenient methods, seamlessly write timing results to an RcppClock object in the R global environment, and summarize and/or plot the results in R.

R-level and C++-level functionality to generate random deviates from and calculate moments of a Truncated Normal distribution using the algorithm of Robert (1995) <DOI:10.1007/BF00143942>. In addition to RNG, functions for calculating moments, densities, and entropies are provided at both levels.

Implementation of the affine-invariant method of Goodman & Weare (2010) <DOI:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.

Traditional latent variable models assume that the population is homogeneous, meaning that all individuals in the population are assumed to have the same latent structure. However, this assumption is often violated in practice given that individuals may differ in their age, gender, socioeconomic status, and other factors that can affect their latent structure. The robust expectation maximization (REM) algorithm is a statistical method for estimating the parameters of a latent variable model in the presence of population heterogeneity as recommended by Nieser & Cochran (2023) <doi:10.1037/met0000413>. The REM algorithm is based on the expectation-maximization (EM) algorithm, but it allows for the case when all the data are generated by the assumed data generating model.

The Public Trading API <https://public.com/api/docs> allows clients to access their brokerage accounts, request market data, and place stock/etf/option orders.

Simple, easy to use, and flexible functionality for recoding variables. It allows for simple piecewise definition of transformations.

This package provides a set of tools for working with Romanian personal numeric codes. The core is a validation function which applies several verification criteria to assess the validity of numeric codes. This is accompanied by functionality for extracting the different components of a personal numeric code. A personal numeric code is issued to all Romanian residents either at birth or when they obtain a residence permit.

Compute the repeated measures correlation, a statistical technique for determining the overall within-individual relationship among paired measures assessed on two or more occasions, first introduced by Bland and Altman (1995). Includes functions for diagnostics, p-value, effect size with confidence interval including optional bootstrapping, as well as graphing. Also includes several example datasets. For more details, see the web documentation <https://lmarusich.github.io/rmcorr/index.html> and the original paper: Bakdash and Marusich (2017) <doi:10.3389/fpsyg.2017.00456>.

This package implements the pseudo-R2D2 prior for ordinal regression from the paper "Pseudo-R2D2 prior for high-dimensional ordinal regression" by Yanchenko (2025) <doi:10.1007/s11222-025-10667-x>. In particular, it provides code to evaluate the probability distribution function for the cut-points, compute the log-likelihood, calculate the hyper-parameters for the global variance parameter, find the distribution of McFadden's coefficient-of-determination, and fit the model in rstan'. Please cite the paper if you use these codes.

Use rprofile::load() inside a project .Rprofile file to ensure that the user-global .Rprofile is loaded correctly regardless of its location, and other common resources (in particular renv') are also set up correctly.

Recursive partitioning methods to build classification trees for ordinal responses within the CART framework. Trees are grown using the Generalized Gini impurity function, where the misclassification costs are given by the absolute or squared differences in scores assigned to the categories of the response. Pruning is based on the total misclassification rate or on the total misclassification cost.

Build regular expressions piece by piece using human readable code. This package contains number-related functionality, and is primarily intended to be used by package developers.

Compute an exact CI for the population mean under a random effects model. The routines implement the algorithm described in Michael, Thronton, Xie, and Tian (2017) <https://haben-michael.github.io/research/Exact_Inference_Meta.pdf>.

This package provides access to the xylib C library for to import xy data from powder diffraction, spectroscopy and other experimental methods.

This is a wrapper function for image(), which makes reasonable raster plots with nice axis and other useful features.