Package: pHMC Type: Package Title: Proximal Hamiltonian Monte Carlo for Non-Smooth Bayesian Inference Version: 0.1.0 Authors@R: c( person("Shikhar", "Tyagi", email = "shikhar1093tyagi@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1606-0844")), person("Arvind", "Pandey", email = "arvindmzu@gmail.com", role = "aut"), person("Bhupendra", "Singh", email = "bhupendra.rana@gmail.com", role = "aut"), person("Vrijesh", "Tripathi", email = "vrijesh.tripathi@uwi.edu", role = "aut") ) 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) , 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. License: GPL (>= 2) Encoding: UTF-8 RoxygenNote: 7.3.3 Imports: stats, graphics, grDevices, utils, Matrix Suggests: testthat (>= 3.0.0) Config/testthat/edition: 3 NeedsCompilation: no Packaged: 2026-10-21 09:11:21 UTC; root Author: Shikhar Tyagi [aut, cre] (ORCID: ), Arvind Pandey [aut], Bhupendra Singh [aut], Vrijesh Tripathi [aut] Maintainer: Shikhar Tyagi Repository: https://shikhartyagi.r-universe.dev Date/Publication: 2026-08-21 12:40:09 UTC RemoteUrl: https://github.com/cran/pHMC RemoteRef: HEAD RemoteSha: 70429eb55b931a72f4a362185b64014d3d82a1be