SiER: Signal Extraction Approach for Sparse Multivariate Response Regression

Methods for regression with high-dimensional predictors and univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.

Version:	0.1.0
Suggests:	MASS
Published:	2017-09-19
DOI:	10.32614/CRAN.package.SiER
Author:	Ruiyan, Xin Qi
Maintainer:	Ruiyan Luo <rluo at gsu.edu>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	SiER results

Documentation:

Reference manual:

SiER.pdf

Downloads:

Package source:	SiER_0.1.0.tar.gz
Windows binaries:	r-devel: SiER_0.1.0.zip, r-release: SiER_0.1.0.zip, r-oldrel: SiER_0.1.0.zip
macOS binaries:	r-release (arm64): SiER_0.1.0.tgz, r-oldrel (arm64): SiER_0.1.0.tgz, r-release (x86_64): SiER_0.1.0.tgz, r-oldrel (x86_64): SiER_0.1.0.tgz

Reverse dependencies:

Reverse suggests:

joinet

Linking:

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