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 hotmail.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	visaOTR results

Documentation:

Reference manual:

visaOTR.pdf

Downloads:

Package source:	visaOTR_0.1.0.tar.gz
Windows binaries:	r-devel: visaOTR_0.1.0.zip, r-release: visaOTR_0.1.0.zip, r-oldrel: visaOTR_0.1.0.zip
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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