Functions for range estimation in birds based on Pennycuick (2008)
    and Pennycuick (1975), 'Flight' program which compliments Pennycuick (2008)
    requires manual entry of birds which can be tedious when there are hundreds
    of birds to estimate. Implemented are two ODE methods discussed in Pennycuick (1975)
    and time-marching computation methods as in Pennycuick (1998) and Pennycuick (2008).
    See Pennycuick (1975, ISBN:978-0-12-249405-5), Pennycuick (1998) <doi:10.1006/jtbi.1997.0572>,
    and Pennycuick (2008, ISBN:9780080557816).

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 and probability-based method using
    Masked Language Models to measure conceptual associations
    (e.g., attitudes, biases, stereotypes, social norms, cultural values)
    as propositions 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 a friendly (flexible) Markov Chain Monte Carlo (MCMC)
         framework for implementing Metropolis-Hastings algorithm in a modular way
         allowing users to specify automatic convergence checker, personalized
         transition kernels, and out-of-the-box multiple MCMC chains using
         parallel computing. Most of the methods implemented in this package can
         be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods
         included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5>
         Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5>
         Robust Adaptive Metropolis, and Thawornwattana et
         al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.