Package: rCausalMGM
Type: Package
Title: Scalable Causal Discovery and Model Selection on Mixed Datasets
        with 'rCausalMGM'
Version: 1.0.1
Date: 2026-03-13
Author: Tyler C Lovelace [aut],
  Max Dudek [aut],
  Jack Fiore [aut],
  Panayiotis V Benos [aut, cre]
Authors@R: c(person(given = c("Tyler", "C"),
                    family = "Lovelace",
                    role = "aut"),
	     person(given = "Max",
                    family = "Dudek",
                    role = "aut"),
	     person(given = "Jack",
                    family = "Fiore",
                    role = "aut"),
             person(given = c("Panayiotis", "V"),
                    family = "Benos",
                    role = c("aut", "cre"),
                    email = "pbenos@ufl.edu"))
Maintainer: Panayiotis V Benos <pbenos@ufl.edu>
Description: 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.
License: GPL-3
Imports: Rcpp (>= 1.0.3), survival
LinkingTo: BH, Rcpp, RcppArmadillo, RcppThread
Suggests: Rgraphviz, graph
RoxygenNote: 7.3.2
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2026-03-13 16:17:19 UTC; tyler
Repository: CRAN
Date/Publication: 2026-03-13 19:00:02 UTC
Built: R 4.5.3; x86_64-w64-mingw32; 2026-06-24 02:38:08 UTC; windows
Archs: x64
