itdr: Integral Transformation Methods for SDR in Regression
The itdr() routine allows for the estimation of sufficient dimension reduction subspaces in univariate regression such as the central mean subspace or central subspace in regression. This is achieved using Fourier transformation methods proposed by Zhu and Zeng (2006) <doi:10.1198/016214506000000140>, convolution transformation methods proposed by Zeng and Zhu (2010) <doi:10.1016/j.jmva.2009.08.004>, and iterative Hessian transformation methods proposed by Cook and Li (2002) <doi:10.1214/aos/1021379861>. Additionally, mitdr() function provides optimal estimators for sufficient dimension reduction subspaces in multivariate regression by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) <doi:10.5705/ss.202020.0312>, and selects the sufficient variables using Fourier transform sparse inverse regression estimators proposed by Weng (2022) <doi:10.1016/j.csda.2021.107380>.
| Version: |
2.0.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats, utils, MASS, geigen, magic, energy, tidyr |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2024-02-26 |
| DOI: |
10.32614/CRAN.package.itdr |
| Author: |
Tharindu P. De Alwis
 [aut, cre],
S. Yaser Samadi
[ctb, aut],
Jiaying Weng
[ctb, aut] |
| Maintainer: |
Tharindu P. De Alwis <talwis at wpi.edu> |
| License: |
GPL-2 | GPL-3 |
| NeedsCompilation: |
yes |
| Citation: |
itdr citation info |
| Materials: |
NEWS |
| CRAN checks: |
itdr results |
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