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:	1.0.3
Imports:	Rcpp, data.table, pROC, graphics, mltools, stats, plyr, ggplot2, ClustGeo, wesanderson
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	ggtree, imager
Published:	2021-07-16
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

Documentation:

Reference manual:

CoOL.pdf

Downloads:

Package source:	CoOL_1.0.3.tar.gz
Windows binaries:	r-devel: CoOL_1.0.3.zip, r-release: CoOL_1.0.3.zip, r-oldrel: CoOL_1.0.3.zip
macOS binaries:	r-release (arm64): CoOL_1.0.3.tgz, r-oldrel (arm64): CoOL_1.0.3.tgz, r-release (x86_64): CoOL_1.0.3.tgz, r-oldrel (x86_64): CoOL_1.0.3.tgz
Old sources:	CoOL archive

Linking:

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