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 |
| 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 binaries: | r-release: causalMGM_0.1.1.tgz, r-oldrel: causalMGM_0.1.1.tgz |
| Old sources: | causalMGM archive |
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