Package: PolyTree 0.0.1

Sourav Chatterjee

PolyTree: Estimate Causal Polytree from Data

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) <arxiv:2209.07028>. The method is fully nonparametric, making no use of linearity assumptions, and especially useful when the number of variables is large.

Authors:Sourav Chatterjee [aut, cre]

PolyTree_0.0.1.tar.gz
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PolyTree.pdf |PolyTree.html✨
PolyTree/json (API)

# Install 'PolyTree' in R:
install.packages('PolyTree', repos = 'https://cloud.r-project.org')

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 137 downloads 1 exports 16 dependencies

Last updated 1 years agofrom:510890a7b9. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 21 2025
R-4.5-linuxOKMar 21 2025
R-4.4-linuxOKMar 21 2025

Exports:polytree

Dependencies:clicpp11data.tableFOCIgluegmpigraphlatticelifecyclemagrittrMatrixpkgconfigproxyRANNrlangvctrs

Citation

To cite the methods in the package 'PolyTree' in publications, please cite the following:

Sourav Chatterjee and Mathukumalli Vidyasagar (2022). Estimating large causal polytrees from small samples. arXiv:2209.07028 [Preprint]. Available at https://arxiv.org/abs/2209.07028

Additionally, please cite the 'PolyTree' package:

Corresponding BibTeX entry: 

  @Misc{,
    title = {Estimating large causal polytrees from small samples},
    author = {Sourav Chatterjee and Mathukumalli Vidyasagar},
    journal = {arXiv},
    year = {2022},
    url = {https://arxiv.org/abs/2209.07028},
    note = {arXiv:2209.07028},
  }