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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: |
0.3.0 |
| 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 |
| 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 |
| Citation: |
tehtuner citation info |
| Materials: |
README NEWS |
| CRAN checks: |
tehtuner results |
Documentation:
Downloads:
Linking:
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https://CRAN.R-project.org/package=tehtuner
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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.
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