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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 |
| Author: | Zishu Zhan [aut, cre], Jingxiao Zhang [aut] |
| Maintainer: | Zishu Zhan <zishu927 at hotmail.com> |
| 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: 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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