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 | 
| Maintainer: | Sourav Chatterjee <souravc at stanford.edu> | 
| License: | MIT + file LICENSE | 
| NeedsCompilation: | no | 
| Citation: | PolyTree citation info | 
| CRAN checks: | PolyTree results | 
| Reference manual: | PolyTree.html , 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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