Provides 'R6' abstract classes for building machine learning models 
    with 'scikit-learn' like API. <https://scikit-learn.org/> is a popular module 
    for 'Python' programming language which design became de facto a standard 
    in industry for machine learning tasks.

Implements three bias-correction techniques from Battaglia et al. (2025 <doi:10.48550/arXiv.2402.15585>) to improve inference in regression models with covariates generated by AI or machine learning.

A collection of artificial and real-world machine learning
        benchmark problems, including, e.g., several
        data sets from the UCI repository.

Tools to help visualize Major League Baseball analysis in 'ggplot2' 
  and 'gt'. You provide team/player information and 'mlbplotR' will transform 
  that information into team colors, logos, or player headshots for graphics.

Computational functions for player metrics in major league baseball including batting, pitching, fielding, base-running, and overall player statistics. This package is actively maintained with new metrics being added as they are developed.

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters.

Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them.  This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact. Reference: Knoblauch & Maloney (2012) "Modeling Psychophysical Data in R". <doi:10.1007/978-1-4614-4475-6>.