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Doubly robust methods for evaluating surrogate markers as outlined in: Agniel D, Hejblum BP, Thiebaut R & Parast L (2022). "Doubly robust evaluation of high-dimensional surrogate markers", Biostatistics <doi:10.1093/biostatistics/kxac020>. You can use these methods to determine how much of the overall treatment effect is explained by a (possibly high-dimensional) set of surrogate markers.
Version: | 1.1.1 |
Depends: | R (≥ 3.6.0) |
Imports: | dplyr, gbm, glmnet, glue, parallel, pbapply, purrr, ranger, RCAL, rlang, SIS, stats, SuperLearner, tibble, tidyr |
Published: | 2024-06-14 |
DOI: | 10.32614/CRAN.package.crossurr |
Author: | Denis Agniel [aut, cre], Boris P. Hejblum [aut] |
Maintainer: | Denis Agniel <dagniel at rand.org> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Citation: | crossurr citation info |
Materials: | README NEWS |
CRAN checks: | crossurr results |
Reference manual: | crossurr.pdf |
Package source: | crossurr_1.1.1.tar.gz |
Windows binaries: | r-devel: crossurr_1.1.1.zip, r-release: crossurr_1.1.1.zip, r-oldrel: crossurr_1.1.1.zip |
macOS binaries: | r-release (arm64): crossurr_1.1.1.tgz, r-oldrel (arm64): crossurr_1.1.1.tgz, r-release (x86_64): crossurr_1.1.1.tgz, r-oldrel (x86_64): crossurr_1.1.1.tgz |
Old sources: | crossurr 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.