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dmrfit
View on CRAN: Click
here
Download and install dmrfit package within the R console
Install from CRAN:
install.packages("dmrfit")
Install from Github:
library("remotes")
install_github("cran/dmrfit") Install by package version:
library("remotes")
install_version("dmrfit", "0.1.1") Attach the package and use:
library("dmrfit")
Maintained by
Giuseppe Arena
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-10-22
Latest Update: 2026-10-22
Description:
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.
How to cite:
Giuseppe Arena (2026). dmrfit: Scalable Bayesian Inference for Discrete Markov Random Fields. R package version 0.1.1, https://cran.r-project.org/web/packages/dmrfit. Accessed 25 Oct. 2026.
Previous versions and publish date:
(2026-10-22 15:20), 0.1.0 (2026-10-18 17:10)
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