https://pbenos-uf.r-universe.devRecent package updates in pbenos-ufR-universehttps://github.com/pbenos-uf.pnghttps://pbenos-uf.r-universe.devFri, 13 Mar 2026 19:00:02 GMTpbenos@ufl.edu (Panayiotis V Benos)Scalable methods for learning causal graphical models from mixed data, including continuous, discrete, and censored variables. The package implements CausalMGM, which combines a convex, score-based approach for learning an initial moralized graph with a producer-consumer scheme that enables efficient parallel conditional independence testing in constraint-based causal discovery algorithms. The implementation supports high-dimensional datasets and provides individual access to core components of the workflow, including MGM and the PC-Stable and FCI-Stable causal discovery algorithms. To support practical applications, the package includes multiple model selection strategies, including information criteria based on likelihood and model complexity, cross-validation for out-of-sample likelihood estimation, and stability-based approaches that assess graph robustness across subsamples.https://github.com/r-universe/pbenos-uf/actions/runs/38397934072Fri, 13 Mar 2026 19:00:02 GMTrCausalMGM1.0.1successhttps://pbenos-uf.r-universe.devhttps://github.com/cran/rCausalMGM