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crossurr  

Cross-Fitting for Doubly Robust Evaluation of High-Dimensional Surrogate Markers
View on CRAN: Click here


Download and install crossurr package within the R console
Install from CRAN:
install.packages("crossurr")

Install from Github:
library("remotes")
install_github("cran/crossurr")

Install by package version:
library("remotes")
install_version("crossurr", "1.1.2")



Attach the package and use:
library("crossurr")
Maintained by
Denis Agniel
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-10-21
Latest Update: 2025-04-08
Description:
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 . 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.
How to cite:
Denis Agniel (2022). crossurr: Cross-Fitting for Doubly Robust Evaluation of High-Dimensional Surrogate Markers. R package version 1.1.2, https://cran.r-project.org/web/packages/crossurr. Accessed 29 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:29), 1.0.6 (2022-10-21 17:25), 1.1.1 (2024-06-14 23:20)
Other packages that cited crossurr R package
View crossurr citation profile
Other R packages that crossurr depends, imports, suggests or enhances
Complete documentation for crossurr
Functions, R codes and Examples using the crossurr R package
Some associated functions: crossurr-package . lasso . ols . sim_data . xf_surrogate . xfit_dr . xfr_surrogate . 
Some associated R codes: crossurr-package.R . fold_fit.R . lasso.R . parametric-models.R . sim_data.R . sis.R . super_learn.R . xf_surrogate.R . xfit.R . xfit_dr.R . xfit_lasso.R . xfit_sis.R . xfit_sl.R . xfr_surrogate.R .  Full crossurr package functions and examples
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