Computation of predictive information criteria (PIC) from select model object classes 
    for model selection in predictive contexts. In contrast to the more widely used Akaike 
    Information Criterion (AIC), which are derived under the assumption that target(s) of prediction 
    (i.e. validation data) are independently and identically distributed to the fitting data, the PIC
    are derived under less restrictive assumptions and thus generalize AIC to the more practically 
    relevant case of training/validation data heterogeneity. The methodology featured in this package is based on Flores (2021) <https://iro.uiowa.edu/esploro/outputs/doctoral/A-new-class-of-information-criteria/9984097169902771?institution=01IOWA_INST> "A new class of information criteria for improved prediction in the presence of training/validation data heterogeneity".

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>.

This function fits a reversible jump Bayesian piecewise exponential model that also includes the intensity of each event considered along with the rate of events.

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.