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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: | 1.0.6 |
| Depends: | R (≥ 3.6.0) |
| Imports: | dplyr, gbm, glmnet, glue, parallel, pbapply, purrr, ranger, RCAL, rlang, SIS, stats, SuperLearner, tibble, tidyr |
| Published: | 2022-10-21 |
| 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_1.0.6.tar.gz |
| Windows binaries: | r-devel: crossurr_1.0.6.zip, r-release: crossurr_1.0.6.zip, r-oldrel: crossurr_1.0.6.zip |
| macOS binaries: | r-release (arm64): crossurr_1.0.6.tgz, r-oldrel (arm64): crossurr_1.0.6.tgz, r-release (x86_64): crossurr_1.0.6.tgz, r-oldrel (x86_64): crossurr_1.0.6.tgz |
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These binaries (installable software) and packages are in development.
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