HCR: Causal Discovery from Discrete Data using Hidden Compact Representation

This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data. We implement an effective solution to recover the above hidden compact representation under the likelihood framework. Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.

Version: 0.1.1
Imports: data.table (≥ 1.10.4), methods
Published: 2018-10-26
DOI: 10.32614/CRAN.package.HCR
Author: Jie Qiao [aut, cre], Ruichu Cai [ths, aut], Kun Zhang [ths, aut], Zhenjie Zhang [ths, aut], Zhifeng Hao [ths, aut]
Maintainer: Jie Qiao <qiaojie.chn at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: HCR citation info
Materials: README NEWS
CRAN checks: HCR results


Reference manual: HCR.pdf


Package source: HCR_0.1.1.tar.gz
Windows binaries: r-devel: HCR_0.1.1.zip, r-release: HCR_0.1.1.zip, r-oldrel: HCR_0.1.1.zip
macOS binaries: r-release (arm64): HCR_0.1.1.tgz, r-oldrel (arm64): HCR_0.1.1.tgz, r-release (x86_64): HCR_0.1.1.tgz, r-oldrel (x86_64): HCR_0.1.1.tgz
Old sources: HCR archive


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