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: | 1.0.2 |
| Imports: | Rcpp, data.table, pROC, graphics, mltools, stats, plyr, ggplot2, ClustGeo, wesanderson |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | ggtree, imager |
| Published: | 2021-06-30 |
| 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 |
| Reference manual: | CoOL.pdf |
| Package source: | CoOL_1.0.2.tar.gz |
| Windows binaries: | r-devel: CoOL_1.0.1.zip, r-devel-UCRT: CoOL_1.0.1.zip, r-release: CoOL_1.0.1.zip, r-oldrel: CoOL_1.0.1.zip |
| macOS binaries: | r-release (arm64): CoOL_1.0.1.tgz, r-release (x86_64): CoOL_1.0.1.tgz, r-oldrel: CoOL_1.0.1.tgz |
| Old sources: | CoOL archive |
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