Package: CoOL
Type: Package
Title: Causes of Outcome Learning
Version: 1.1.2
Date: 2022-05-23
Authors@R: c(
   person("Andreas", "Rieckmann", role = c("aut","cre"), email = "aric@sund.ku.dk"),
   person("Piotr", "Dworzynski", role = "aut", email = "piotr@sund.ku.dk"),
   person("Leila", "Arras", role = "ctb", email = "leila.arras@hhi.fraunhofer.de"),
   person(c("Claus","Thorn"), "Ekstrom", role = "aut", email = "ekstrom@sund.ku.dk"))
Maintainer: Andreas Rieckmann <aric@sund.ku.dk>
Description: 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.
URL: https://bioconductor.org
License: GPL-2
Imports: Rcpp, data.table, pROC, graphics, mltools, stats, plyr,
        ggplot2, ClustGeo, wesanderson, grDevices
Suggests: ggtree, imager
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
RoxygenNote: 7.2.0
NeedsCompilation: yes
Packaged: 2022-05-24 09:34:01 UTC; lvb917
Author: Andreas Rieckmann [aut, cre],
  Piotr Dworzynski [aut],
  Leila Arras [ctb],
  Claus Thorn Ekstrom [aut]
Repository: CRAN
Date/Publication: 2022-05-24 10:20:05 UTC
Built: R 4.5.1; x86_64-w64-mingw32; 2025-10-06 03:05:29 UTC; windows
Archs: x64
