# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "dmrfit" in publications use:' type: software license: MIT title: 'dmrfit: Scalable Bayesian Inference for Discrete Markov Random Fields' version: 0.1.1 doi: 10.32614/CRAN.package.dmrfit abstract: Estimation of discrete Markov random fields through the pseudo-likelihood, for both Ising models (Ising (1925) ) and ordinal Markov random fields (Marsman et al. (2025) ). Point estimates, either maximum pseudo-likelihood or maximum a posteriori, are obtained through a trust region optimization algorithm, with robust standard errors from the Godambe-Huber-White (GHW, or sandwich) estimator, for both full and constrained network structures. Bayesian inference corrects the pseudo-posterior through coordinate rescaling (Arena and Marsman (2026) ), which matches its covariance to the GHW covariance, and samples from it with the Fisher adaptive Metropolis-adjusted Langevin algorithm (Titsias (2024) ). For comparison, the posterior based on the full likelihood can also be sampled, exactly for small networks or with the double Metropolis-Hastings algorithm (Liang (2010) ). Savage-Dickey Bayes factors test each pairwise interaction, computed either by sampling importance resampling (Skare et al. (2003) ) or from the posterior draws. authors: - family-names: Arena given-names: Giuseppe email: g.arena@uva.nl orcid: https://orcid.org/0000-0001-5204-3326 repository: https://jupepis.r-universe.dev repository-code: https://github.com/jupepis/dmrfit commit: 213b24c4b9bb6755135df17fb122d35455130d13 url: https://jupepis.github.io/dmrfit/ date-released: '2026-10-21' contact: - family-names: Arena given-names: Giuseppe email: g.arena@uva.nl orcid: https://orcid.org/0000-0001-5204-3326