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>.

Getting started

Package details

AuthorAndrew J Sedgewick, Neha Abraham <[email protected]>, Vineet Raghu <[email protected]>, Panagiotis Benos <[email protected]>
MaintainerNeha Abraham <[email protected]>
LicenseGPL-2
Version0.1.1
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("causalMGM")

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causalMGM documentation built on Sept. 14, 2017, 5:05 p.m.