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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "PolyTree" in publications use:'
type: software
license: MIT
title: 'PolyTree: Estimate Causal Polytree from Data'
version: 0.0.1
identifiers:
- type: doi
  value: 10.32614/CRAN.package.PolyTree
abstract: 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:
- family-names: Chatterjee
  given-names: Sourav
  email: souravc@stanford.edu
  orcid: https://orcid.org/0000-0003-4460-209X
preferred-citation:
  type: generic
  title: Estimating large causal polytrees from small samples
  authors:
  - family-names: Chatterjee
    given-names: Sourav
    email: souravc@stanford.edu
    orcid: https://orcid.org/0000-0003-4460-209X
  - family-names: Vidyasagar
    given-names: Mathukumalli
  journal: arXiv
  year: '2022'
  url: https://arxiv.org/abs/2209.07028
  notes: arXiv:2209.07028
repository: https://souravc1979.r-universe.dev
commit: 510890a7b9a8aa28984de079e24ff43cc4ce5e4c
date-released: '2024-03-26'
contact:
- family-names: Chatterjee
  given-names: Sourav
  email: souravc@stanford.edu
  orcid: https://orcid.org/0000-0003-4460-209X