crossurr: Cross-Fitting for Doubly Robust Evaluation of High-Dimensional Surrogate Markers

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>
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:, r-release:, r-oldrel:
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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