tehtuner: Fit and Tune Models to Detect Treatment Effect Heterogeneity

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: 0.1.1
Depends: R (≥ 3.5.0)
Imports: party, glmnet, Rdpack, rpart, stringr, SuperLearner, randomForestSRC, earth
Suggests: spelling, testthat (≥ 3.0.0)
Published: 2022-11-07
Author: Jack Wolf ORCID iD [aut, cre]
Maintainer: Jack Wolf <jackwolf910 at gmail.com>
BugReports: https://github.com/jackmwolf/tehtuner/issues
License: GPL (≥ 3)
URL: https://github.com/jackmwolf/tehtuner
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: tehtuner results


Reference manual: tehtuner.pdf


Package source: tehtuner_0.1.1.tar.gz
Windows binaries: r-devel: tehtuner_0.1.1.zip, r-release: tehtuner_0.1.1.zip, r-oldrel: tehtuner_0.1.1.zip
macOS binaries: r-release (arm64): tehtuner_0.1.1.tgz, r-oldrel (arm64): tehtuner_0.1.1.tgz, r-release (x86_64): tehtuner_0.1.1.tgz, r-oldrel (x86_64): tehtuner_0.1.1.tgz


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