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. 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.

Getting started

Package details

AuthorAndreas Rieckmann [aut, cre], Piotr Dworzynski [aut], Leila Arras [ctb], Claus Thorn Ekstrom [aut]
MaintainerAndreas Rieckmann <aric@sund.ku.dk>
LicenseGPL-2
Version1.0.1
URL https://bioconductor.org
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("CoOL")

Try the CoOL package in your browser

Any scripts or data that you put into this service are public.

CoOL documentation built on Feb. 23, 2021, 5:12 p.m.