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 |
| DOI: |
10.32614/CRAN.package.CoOL |
| 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:
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