Package: causalMGM
Type: Package
Title: Causal Learning of Mixed Graphical Models
Version: 0.1.0
Author: Andrew J Sedgewick, Neha Abraham <neha.abraham@pitt.edu>, Vineet Raghu <vineetraghu@gmail.com>, Panagiotis Benos <benos@pitt.edu>
Maintainer: Neha Abraham <mgmquery@pitt.edu>
Description: 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>.
License: GPL-2
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.2.0), rJava
SystemRequirements: Java (>= 1.7), JRI
NeedsCompilation: no
Packaged: 2017-08-29 18:57:59 UTC; nea24
Repository: CRAN
Date/Publication: 2017-08-30 08:28:56 UTC
