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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=hmclearn to link to this page.
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