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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" <doi:10.48550/arXiv.1701.02434>, Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <doi:10.48550/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.
Version: | 0.0.5 |
Depends: | R (≥ 3.6) |
Imports: | bayesplot, parallel, MASS, mvtnorm |
Suggests: | knitr, rmarkdown, Matrix, lme4, carData, mlbench, ggplot2, mlmRev, testthat, MCMCpack |
Published: | 2020-10-05 |
DOI: | 10.32614/CRAN.package.hmclearn |
Author: | Samuel Thomas [cre, aut], Wanzhu Tu [ctb] |
Maintainer: | Samuel Thomas <samthoma at iu.edu> |
License: | GPL-3 |
NeedsCompilation: | no |
Language: | en-US |
Materials: | README NEWS |
CRAN checks: | hmclearn results |
Reference manual: | hmclearn.pdf |
Vignettes: |
linear_mixed_effects_hmclearn linear_regression_hmclearn logistic_mixed_effects_hmclearn logistic_regression_hmclearn poisson_regression_hmclearn |
Package source: | hmclearn_0.0.5.tar.gz |
Windows binaries: | r-devel: hmclearn_0.0.5.zip, r-release: hmclearn_0.0.5.zip, r-oldrel: hmclearn_0.0.5.zip |
macOS binaries: | r-release (arm64): hmclearn_0.0.5.tgz, r-oldrel (arm64): hmclearn_0.0.5.tgz, r-release (x86_64): hmclearn_0.0.5.tgz, r-oldrel (x86_64): hmclearn_0.0.5.tgz |
Old sources: | hmclearn archive |
Please use the canonical form https://CRAN.R-project.org/package=hmclearn to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.