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. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology <doi:10.1093/ije/dyac078>. The optional 'ggtree' package can be obtained through Bioconductor.

Version: 1.1.2
Imports: Rcpp, data.table, pROC, graphics, mltools, stats, plyr, ggplot2, ClustGeo, wesanderson, grDevices
LinkingTo: Rcpp, RcppArmadillo
Suggests: ggtree, imager
Published: 2022-05-24
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_1.1.2.tar.gz
Windows binaries: r-devel: CoOL_1.1.2.zip, r-release: CoOL_1.0.3.zip, r-oldrel: CoOL_1.1.2.zip
macOS binaries: r-release (arm64): CoOL_1.1.2.tgz, r-oldrel (arm64): CoOL_1.1.2.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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