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In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
| Version: | 1.1 |
| Imports: | methods |
| Published: | 2020-07-04 |
| DOI: | 10.32614/CRAN.package.EffectTreat |
| Author: | Wim Van der Elst, Ariel Alonso & Geert Molenberghs |
| Maintainer: | Wim Van der Elst <Wim.vanderelst at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | CausalInference |
| CRAN checks: | EffectTreat results |
| Reference manual: | EffectTreat.pdf |
| Package source: | EffectTreat_1.1.tar.gz |
| Windows binaries: | r-devel: EffectTreat_1.1.zip, r-release: EffectTreat_1.1.zip, r-oldrel: EffectTreat_1.1.zip |
| macOS binaries: | r-release (arm64): EffectTreat_1.1.tgz, r-oldrel (arm64): EffectTreat_1.1.tgz, r-release (x86_64): EffectTreat_1.1.tgz, r-oldrel (x86_64): EffectTreat_1.1.tgz |
| Old sources: | EffectTreat archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.