CVarE: Conditional Variance Estimator for Sufficient Dimension Reduction

Implementation of the CVE (Conditional Variance Estimation) method proposed by Fertl, L. and Bura, E. (2021) <arXiv:2102.08782> and the ECVE (Ensemble Conditional Variance Estimation) method introduced in Fertl, L. and Bura, E. (2021) <arXiv:2102.13435>. CVE and ECVE are sufficient dimension reduction methods in regressions with continuous response and predictors. CVE applies to general additive error regression models while ECVE generalizes to non-additive error regression models. They operate under the assumption that the predictors can be replaced by a lower dimensional projection without loss of information. It is a semiparametric forward regression model based exhaustive sufficient dimension reduction estimation method that is shown to be consistent under mild assumptions.

Version: 1.1
Imports: stats, mda
Published: 2021-03-11
Author: Daniel Kapla [aut, cph, cre], Lukas Fertl [aut, cph], Efstathia Bura [ctb]
Maintainer: Daniel Kapla <daniel at kapla.at>
Contact: <daniel@kapla.at>
License: GPL-3
URL: https://git.art-ist.cc/daniel/CVE
NeedsCompilation: yes
CRAN checks: CVarE results

Downloads:

Reference manual: CVarE.pdf
Package source: CVarE_1.1.tar.gz
Windows binaries: r-devel: CVarE_1.1.zip, r-devel-UCRT: CVarE_1.1.zip, r-release: CVarE_1.1.zip, r-oldrel: CVarE_1.1.zip
macOS binaries: r-release (arm64): CVarE_1.1.tgz, r-release (x86_64): CVarE_1.1.tgz, r-oldrel: CVarE_1.1.tgz

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