The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=visaOTR to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.