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Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2017) <doi:10.48550/arXiv.1611.06655>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.
| Version: | 0.1.1 |
| Imports: | glmnet, graphics, stats |
| Published: | 2017-12-06 |
| DOI: | 10.32614/CRAN.package.LassoSIR |
| Author: | Zhigen Zhao, Qian Lin, Jun Liu |
| Maintainer: | Zhigen Zhao <zhigen.zhao at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | LassoSIR results |
| Reference manual: | LassoSIR.pdf |
| Package source: | LassoSIR_0.1.1.tar.gz |
| Windows binaries: | r-devel: LassoSIR_0.1.1.zip, r-release: LassoSIR_0.1.1.zip, r-oldrel: LassoSIR_0.1.1.zip |
| macOS binaries: | r-release (arm64): LassoSIR_0.1.1.tgz, r-oldrel (arm64): LassoSIR_0.1.1.tgz, r-release (x86_64): LassoSIR_0.1.1.tgz, r-oldrel (x86_64): LassoSIR_0.1.1.tgz |
| Old sources: | LassoSIR archive |
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