# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "pHMC" in publications use:' type: software license: GPL-2.0-or-later title: 'pHMC: Proximal Hamiltonian Monte Carlo for Non-Smooth Bayesian Inference' version: 0.1.0 doi: 10.32614/CRAN.package.pHMC abstract: 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. authors: - family-names: Tyagi given-names: Shikhar email: shikhar1093tyagi@gmail.com orcid: https://orcid.org/0000-0003-1606-0844 - family-names: Pandey given-names: Arvind email: arvindmzu@gmail.com - family-names: Singh given-names: Bhupendra email: bhupendra.rana@gmail.com - family-names: Tripathi given-names: Vrijesh email: vrijesh.tripathi@uwi.edu repository: https://shikhartyagi.r-universe.dev commit: 70429eb55b931a72f4a362185b64014d3d82a1be date-released: '2026-08-21' contact: - family-names: Tyagi given-names: Shikhar email: shikhar1093tyagi@gmail.com orcid: https://orcid.org/0000-0003-1606-0844