All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) <https://robjhyndman.com/forecasting/>.

Creation of an input model (fitted distribution) via the frequentist model averaging (FMA) approach and generate random-variates from the distribution specified by "myfit" which is the fitted input model via the FMA approach. See W. X. Jiang and B. L. Nelson (2018), "Better Input Modeling via Model Averaging," Proceedings of the 2018 Winter Simulation Conference, IEEE Press, 1575-1586.

The Fill-Mask Association Test ('FMAT')
    <doi:10.1037/pspa0000396>
    is an integrative, probability-based social computing method
    using Masked Language Models to measure conceptual associations
    (e.g., attitudes, biases, stereotypes, social norms, cultural values)
    as propositional semantic representations in natural language.
    Supported language models include 'BERT'
    <doi:10.48550/arXiv.1810.04805> and its variants available at 'Hugging Face'
    <https://huggingface.co/models?pipeline_tag=fill-mask>.
    Methodological references and installation guidance are provided at
    <https://psychbruce.github.io/FMAT/>.

Generate cost effective minimally changed run sequences 
              for symmetrical as well as asymmetrical factorial 
              designs.

Provides an implementation of finite mixture regression models for censored
    data under four distributional families: Normal (FM-NCR), Student t (FM-TCR),
    skew-Normal (FM-SNCR), and skew-t (FM-STCR). The package enables flexible
    modeling of skewness and heavy tails often observed in real-world data, while
    explicitly accounting for censoring. Functions are included for parameter
    estimation via the Expectation-Maximization (EM) algorithm, computation of standard errors, and model
    comparison criteria such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), 
    and the Efficient Determination Criterion (EDC). The underlying methodology is described in Park et al. (2024)
    <doi:10.1007/s00180-024-01459-4>.