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pHMC  

Proximal Hamiltonian Monte Carlo for Non-Smooth Bayesian Inference
View on CRAN: Click here


Download and install pHMC package within the R console
Install from CRAN:
install.packages("pHMC")

Install from Github:
library("remotes")
install_github("cran/pHMC")

Install by package version:
library("remotes")
install_version("pHMC", "0.1.0")



Attach the package and use:
library("pHMC")
Maintained by
Shikhar Tyagi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-08-21
Latest Update: 2026-08-21
Description:
Implements the Proximal Hamiltonian Monte Carlo (p-HMC) algorithm for Bayesian sampling and estimation from non-differentiable target densities. The method decomposes a target potential into a smooth component f(x) and a non-smooth convex component g(x), approximating only g(x) via its Moreau-Yosida envelope while retaining exact gradient information for f(x). This approach, based on the methodology described in Shukla, Vats, and Chi (2025) <doi:10.48550/arXiv.2510.22252>, yields improved Hamiltonian conservation over full-potential smoothing approaches. The package provides generalized routines accepting user-defined probability density functions, log-likelihoods, priors, and proximal operators, together with automated hyperparameter tuning for the Moreau-Yosida regularization parameter, Markov chain Monte Carlo convergence diagnostics, effective sample size computation, and model evaluation metrics including the Akaike information criterion and Bayesian information criterion.
How to cite:
Shikhar Tyagi (2026). pHMC: Proximal Hamiltonian Monte Carlo for Non-Smooth Bayesian Inference. R package version 0.1.0, https://cran.r-project.org/web/packages/pHMC. Accessed 24 Oct. 2026.
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