Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models.  All of the code is written in R.  Theoretical references are listed below:.
Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418,
Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" <arXiv:1701.02434>,
Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>,
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955,
Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034,
Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.

The H-Measure and Other Scalar Classification Performance
Metrics
Christoforos Anagnostopoulos
<christoforos.anagnostopoulos06@imperial.ac.uk> and David J.
Hand <d.j.hand@imperial.ac.uk>
Classification performance metrics that are derived from the ROC
curve of a classifier. The package includes the H-measure performance metric
as described in <http://link.springer.com/article/10.1007/s10994-009-5119-5>,
which computes the minimum total misclassification cost, integrating over any
uncertainty about the relative misclassification costs, as per a user-defined
prior. It also offers a one-stop-shop for other scalar metrics of performance,
including sensitivity, specificity and many others, and also offers plotting
tools for ROC curves and related statistics.

The H-measure and other scalar classification performance
metrics
Scalar performance metrics, including the H-measure, based
on classification scores for several classifiers applied to the
same dataset.

c(person("Andrew", "Iskauskas", email = "andrew.iskauskas@durham.ac.uk", comment = c(ORCID = "0000-0003-2825-3651"), role = c('aut', 'cre')))
A set of objects and functions for Bayes Linear emulation and history matching.
Core functionality includes automated training of emulators to data, diagnostic functions
to ensure suitability, and a variety of proposal methods for generating 'waves' of points.
For details on the mathematical background, there are many papers available on the topic
(see references attached to function help files); for details of the functions in this package,
consult the manual or help files.
purrr, stringr, tidyr, ggplot2, lhs, MASS, R6, viridis,
mvtnorm, GGally, rlang, isoband, mclust
Andrew Iskauskas [aut, cre] (<https://orcid.org/0000-0003-2825-3651>)