Scalable Causal Discovery and Model Selection on Mixed Datasets with 'rCausalMGM' R package rCausalMGM version 1.0.1 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. Software Imports: Rcpp (>= 1.0.3), survival LinkingTo: BH, Rcpp, RcppArmadillo, RcppThread Suggests: Rgraphviz, graph Panayiotis V Benos <pbenos@ufl.edu> Comprehensive R Archive Network (CRAN) Tyler C Lovelace [aut], Max Dudek [aut], Jack Fiore [aut], Panayiotis V Benos [aut, cre] GPL-3 2026-03-13 application/tgz https://CRAN.R-project.org/package=rCausalMGM doi:10.32614/CRAN.package.rCausalMGM