Sparse regression and classification via the Pivotal
    Information Criterion (PIC), an alternative to the Bayesian
    Information Criterion (BIC), cross-validation, and Lasso-based
    tuning. The regularization parameter is selected from a pivotal
    null-distribution statistic, eliminating the need for
    cross-validation and yielding sharper support recovery. Provides
    Fast Iterative Shrinkage-Thresholding Algorithm (FISTA)
    optimization for the L1, Smoothly Clipped Absolute Deviation
    (SCAD), and Minimax Concave Penalty (MCP) penalties across six
    response distributions: Gaussian, binomial, Poisson, exponential,
    Gumbel, and Cox. Under standard sparsity assumptions, the
    selector achieves a phase transition for exact support recovery,
    analogous to results in compressed sensing. See Sardy, van Cutsem
    and van de Geer (2026) <doi:10.48550/arXiv.2603.04172>.

A collection of scripts and data files for the statistics text:
 "Process Improvement using Data" <https://learnche.org/pid/> and the online
 course "Experimentation for Improvement" found on Coursera. The package
 contains code for designed experiments, data sets and other convenience
 functions used in the book.

Implements a novel predictive model, Partially Interpretable Estimators (PIE), which jointly trains an interpretable model and a black-box model to achieve high predictive performance as well as partial model. See the paper, Wang, Yang, Li, and Wang (2021) <doi:10.48550/arXiv.2105.02410>.

Tokenizers break text into pieces that are more usable by
    machine learning models. Many tokenizers share some preparation steps.
    This package provides those shared steps, along with a simple
    tokenizer.

An 'R6' class to set up, run, monitor, collate, and debug large simulation studies comprising many small independent replications and treatment configurations. Parallel processing, reproducibility, fault- and error-tolerance, and ability to resume an interrupted or timed-out simulation study are built in.