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 [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

Documentation:

Reference manual:

tehtuner.pdf

Downloads:

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

Linking:

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