Fit Statistical Models Using Hamiltonian Monte Carlo R package hmclearn version 0.0.5 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. Software Depends: R (>= 3.6) Imports: bayesplot, parallel, MASS, mvtnorm Suggests: knitr, rmarkdown, Matrix, lme4, carData, mlbench, ggplot2, mlmRev, testthat, MCMCpack Samuel Thomas <samthoma@iu.edu> Comprehensive R Archive Network (CRAN) Samuel Thomas [cre, aut], Wanzhu Tu [ctb] GPL-3 2020-10-05 application/tgz https://CRAN.R-project.org/package=hmclearn doi:10.32614/CRAN.package.hmclearn en-US