itdr
2.0.1Integral Transformation Methods for SDR in Regression
Overview
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.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 50%
- Documented parameters
- 99%
- Return-value docs
- 100%
- References docs
- 64%
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Code & Tests
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Package metadata
- First published
- 2021-07-23
- Total releases
- 4 / 5 yrs
- License
- GPL-2 | GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 971 KB / 5 files
- Download size
- 1.0 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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