Package: dmrfit 0.1.1

dmrfit: Scalable Bayesian Inference for Discrete Markov Random Fields

Estimation of discrete Markov random fields through the pseudo-likelihood, for both Ising models (Ising (1925) <doi:10.1007/BF02980577>) and ordinal Markov random fields (Marsman et al. (2025) <doi:10.1017/psy.2024.4>). 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) <doi:10.48550/arXiv.2601.17205>), which matches its covariance to the GHW covariance, and samples from it with the Fisher adaptive Metropolis-adjusted Langevin algorithm (Titsias (2024) <doi:10.48550/arXiv.2305.14442>). 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) <doi:10.1080/00949650902882162>). Savage-Dickey Bayes factors test each pairwise interaction, computed either by sampling importance resampling (Skare et al. (2003) <doi:10.1111/1467-9469.00360>) or from the posterior draws.

Authors:Giuseppe Arena [aut, cre]