causalMGM: Causal Learning of Mixed Graphical Models

Allows users to learn undirected and directed (causal) graphs over mixed data types (i.e., continuous and discrete variables). To learn a directed graph over mixed data, it first calculates the undirected graph (Sedgewick et al, 2016) and then it uses local search strategies to prune-and-orient this graph (Sedgewick et al, 2017). AJ Sedgewick, I Shi, RM Donovan, PV Benos (2016) <doi:10.1186/s12859-016-1039-0>. AJ Sedgewick, JD Ramsey, P Spirtes, C Glymour, PV Benos (2017) <arXiv:1704.02621>.

Version: 0.1.1
Depends: R (≥ 3.2.0), rJava
Published: 2017-09-14
Author: Andrew J Sedgewick, Neha Abraham, Vineet Raghu, Panagiotis Benos
Maintainer: Neha Abraham <mgmquery at pitt.edu>
License: GPL-2
NeedsCompilation: no
SystemRequirements: Java (>= 1.7), JRI
Materials: README
CRAN checks: causalMGM results

Downloads:

Reference manual: causalMGM.pdf
Package source: causalMGM_0.1.1.tar.gz
Windows binaries: r-devel: causalMGM_0.1.1.zip, r-release: causalMGM_0.1.1.zip, r-oldrel: causalMGM_0.1.1.zip
OS X El Capitan binaries: r-release: causalMGM_0.1.1.tgz
OS X Mavericks binaries: r-oldrel: not available
Old sources: causalMGM archive

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