CoOL: Causes of Outcome Learning

Implementing the computational phase of the Causes of Outcome Learning approach as described in Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. medRxiv (2020) <doi:10.1101/2020.12.10.20225243>. The optional 'ggtree' package can be obtained through Bioconductor.

Version: 1.0.1
Imports: Rcpp, data.table, pROC, graphics, mltools, stats, plyr, ggplot2, ClustGeo, wesanderson
LinkingTo: Rcpp, RcppArmadillo
Suggests: ggtree, imager
Published: 2021-02-23
Author: Andreas Rieckmann [aut, cre], Piotr Dworzynski [aut], Leila Arras [ctb], Claus Thorn Ekstrom [aut]
Maintainer: Andreas Rieckmann <aric at sund.ku.dk>
License: GPL-2
URL: https://bioconductor.org
NeedsCompilation: yes
Materials: README
CRAN checks: CoOL results

Downloads:

Reference manual: CoOL.pdf
Package source: CoOL_1.0.1.tar.gz
Windows binaries: r-devel: CoOL_1.0.1.zip, r-release: CoOL_1.0.zip, r-oldrel: CoOL_1.0.zip
macOS binaries: r-release: CoOL_1.0.1.tgz, r-oldrel: CoOL_1.0.tgz
Old sources: CoOL archive

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