nehaabraham/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]>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
nehaabraham/causalMGM documentation built on Nov. 23, 2017, 3:11 a.m.