# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "MYIS" in publications use:' type: software license: GPL-3.0-only title: 'MYIS: ''Moreau-Yosida'' Importance Sampling for Statistical Inference' version: 0.1.0 doi: 10.32614/CRAN.package.MYIS abstract: Implements 'Moreau-Yosida' Markov chain Monte Carlo ('MCMC') importance sampling for parameter estimation and Bayesian inference under smooth, non-differentiable, or light-tailed target posterior distributions and arbitrary probability models with complete or censored data. Users supply user-defined probability density functions, optional distribution functions, parameter ranges, and observations subject to complete, right, left, interval, Type-I, Type-II, progressive Type-II, first-failure, or truncation schemes. Constructs 'Moreau-Yosida' envelopes, gradient-based proposals ('MALA', 'HMC', or 'RWM'), self-normalized importance weights, batch-means asymptotic variance estimates, and Bayesian marginal quantiles. Methodologies are based on 'Shukla', 'Vats', and 'Chi' (2025) , 'Pereyra' (2016) , 'Durmus' and others (2022) , 'Chen' and 'Shao' (1999) , 'Roberts' and 'Rosenthal' (1998) , 'Geweke' (1989) , 'Hesterberg' (1995) , and 'Balakrishnan' and 'Aggarwala' (2000, ISBN:978-0-8176-4001-9). 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: ab601594a1f667f52ead773993fbd5fe19a1e02d date-released: '2026-08-05' contact: - family-names: Tyagi given-names: Shikhar email: shikhar1093tyagi@gmail.com orcid: https://orcid.org/0000-0003-1606-0844