Package: hmclearn
Type: Package
Title: Fit Statistical Models Using Hamiltonian Monte Carlo
Version: 0.0.5
Authors@R: 
    c(person(given = "Samuel",
             family = "Thomas",
             role = c("cre", "aut"),
             email = "samthoma@iu.edu"),
      person(given = "Wanzhu",
             family = "Tu",
             role = "ctb",
             email = "wtu1@iu.edu"))
Maintainer: Samuel Thomas <samthoma@iu.edu>
Description: 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.
Depends: R (>= 3.6)
License: GPL-3
Encoding: UTF-8
Language: en-US
LazyData: true
Suggests: knitr, rmarkdown, Matrix, lme4, carData, mlbench, ggplot2,
        mlmRev, testthat, MCMCpack
RoxygenNote: 7.1.1
Imports: bayesplot, parallel, MASS, mvtnorm
VignetteBuilder: knitr
Config/pak/sysreqs: libicu-dev
Repository: https://sthomas522.r-universe.dev
Date/Publication: 2020-10-04 15:04:58 UTC
RemoteUrl: https://github.com/sthomas522/hmclearn
RemoteRef: HEAD
RemoteSha: c3acc7dabd719edb99c345d2f51f5c65ea170b85
NeedsCompilation: no
Packaged: 2026-10-06 10:34:48 UTC; root
Author: Samuel Thomas [cre, aut],
  Wanzhu Tu [ctb]