doi:10.1002/sim.4322>) for identifying subgroups with differential effects in the context of clinical trials while controlling the probability of falsely detecting a differential effect when the conditional average treatment effect is uniform across the study population using parameter selection methods proposed in Wolf et al. (2022) <doi:10.1177/17407745221095855>.">

tehtuner: Fit and Tune Models to Detect Treatment Effect Heterogeneity (original) (raw)

Implements methods to fit Virtual Twins models (Foster et al. (2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential effects in the context of clinical trials while controlling the probability of falsely detecting a differential effect when the conditional average treatment effect is uniform across the study population using parameter selection methods proposed in Wolf et al. (2022) <doi:10.1177/17407745221095855>.

Version: 0.3.0
Depends: R (≥ 3.5.0)
Imports: party, glmnet, Rdpack, rpart, stringr, SuperLearner, randomForestSRC, earth, foreach
Suggests: knitr, rmarkdown, spelling, testthat (≥ 3.0.0)
Published: 2023-04-01
DOI: 10.32614/CRAN.package.tehtuner
Author: Jack Wolf ORCID iD [aut, cre]
Maintainer: Jack Wolf
BugReports: https://github.com/jackmwolf/tehtuner/issues
License: GPL (≥ 3)
URL: https://github.com/jackmwolf/tehtuner
NeedsCompilation: no
Language: en-US
Citation: tehtuner citation info
Materials: README NEWS
CRAN checks: tehtuner results

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