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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
Browse package contents
Package details |
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| 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> |
| License | GPL-2 |
| Version | 0.1.1 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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