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Description

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" <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.

hmclearn

We developed the R package hmclearn to provide users with a framework to learn the intricacies of the HMC algorithm with hands-on experience by tuning and fitting their own models, with a focus on statistical modeling in particular. The core functions in this package include the Hamiltonian Monte Carlo (HMC) algorithm itself, including functions for the leapfrog, as well as the Metropolis-Hastings (MH) algorithm.

While the core functions are included for both hmc and mh algorithms, users must provide their own functions for the log posterior and, for HMC, the gradient of the log posterior. Default values are provided for the tuning parameters. However, users will likely need to adjust the parameters for their particular applications.

Installation

The most recent hmclearn package can be installed from CRAN via

install.packages("hmclearn")
Metadata

Version

0.0.5

License

Unknown

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