Fit Statistical Models Using Hamiltonian Monte Carlo

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" , Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" , 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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.0.5 by Samuel Thomas, a year ago

Browse source code at

Authors: Samuel Thomas [cre, aut] , Wanzhu Tu [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports bayesplot, parallel, MASS, mvtnorm

Suggests knitr, rmarkdown, Matrix, lme4, carData, mlbench, ggplot2, mlmRev, testthat, MCMCpack

See at CRAN