visaOTR: Valid Improved Sparsity A-Learning for Optimal Treatment Decision

Valid Improved Sparsity A-Learning (VISA) provides a new method for selecting important variables involved in optimal treatment regime from a multiply robust perspective. The VISA estimator achieves its success by borrowing the strengths of both model averaging (ARM, Yuhong Yang, 2001) <doi:10.1198/016214501753168262> and variable selection (PAL, Chengchun Shi, Ailin Fan, Rui Song and Wenbin Lu, 2018) <doi:10.1214/17-AOS1570>. The package is an implementation of Zishu Zhan and Jingxiao Zhang. (2022+).

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: Rglpk, e1071, kernlab, Matrix, mboost, randomForest, stats, xgboost
Published: 2022-07-08
DOI: 10.32614/CRAN.package.visaOTR
Author: Zishu Zhan [aut, cre], Jingxiao Zhang [aut]
Maintainer: Zishu Zhan <zishu927 at>
License: GPL-3
NeedsCompilation: no
CRAN checks: visaOTR results


Reference manual: visaOTR.pdf


Package source: visaOTR_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): visaOTR_0.1.0.tgz, r-oldrel (arm64): visaOTR_0.1.0.tgz, r-release (x86_64): visaOTR_0.1.0.tgz, r-oldrel (x86_64): visaOTR_0.1.0.tgz


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