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Given a data matrix with rows representing data vectors and columns representing variables, produces a directed polytree for the underlying causal structure. Based on the algorithm developed in Chatterjee and Vidyasagar (2022) <doi:10.48550/arXiv.2209.07028>. The method is fully nonparametric, making no use of linearity assumptions, and especially useful when the number of variables is large.
Version: | 0.0.1 |
Imports: | FOCI, igraph |
Published: | 2024-03-25 |
DOI: | 10.32614/CRAN.package.PolyTree |
Author: | Sourav Chatterjee [aut, cre] |
Maintainer: | Sourav Chatterjee <souravc at stanford.edu> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Citation: | PolyTree citation info |
CRAN checks: | PolyTree results |
Reference manual: | PolyTree.pdf |
Package source: | PolyTree_0.0.1.tar.gz |
Windows binaries: | r-devel: PolyTree_0.0.1.zip, r-release: PolyTree_0.0.1.zip, r-oldrel: PolyTree_0.0.1.zip |
macOS binaries: | r-release (arm64): PolyTree_0.0.1.tgz, r-oldrel (arm64): PolyTree_0.0.1.tgz, r-release (x86_64): PolyTree_0.0.1.tgz, r-oldrel (x86_64): PolyTree_0.0.1.tgz |
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