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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=CoOL to link to this page.
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