catch: Covariate-Adjusted Tensor Classification in High-Dimensions

Performs classification and variable selection on high-dimensional tensors (multi-dimensional arrays) after adjusting for additional covariates (scalar or vectors) as CATCH model in Pan, Mai and Zhang (2018) <doi:10.48550/arXiv.1805.04421>. The low-dimensional covariates and the high-dimensional tensors are jointly modeled to predict a categorical outcome in a multi-class discriminant analysis setting. The Covariate-Adjusted Tensor Classification in High-dimensions (CATCH) model is fitted in two steps: (1) adjust for the covariates within each class; and (2) penalized estimation with the adjusted tensor using a cyclic block coordinate descent algorithm. The package can provide a solution path for tuning parameter in the penalized estimation step. Special case of the CATCH model includes linear discriminant analysis model and matrix (or tensor) discriminant analysis without covariates.

Version:	1.0.1
Depends:	R (≥ 3.1.1)
Imports:	tensr, Matrix, MASS, methods
Published:	2021-01-04
DOI:	10.32614/CRAN.package.catch
Author:	Yuqing Pan, Qing Mai, Xin Zhang
Maintainer:	Yuqing Pan <yuqing.pan at stat.fsu.edu>
License:	GPL-2
NeedsCompilation:	yes
CRAN checks:	catch results

Documentation:

Reference manual:

catch.pdf

Downloads:

Package source:	catch_1.0.1.tar.gz
Windows binaries:	r-devel: catch_1.0.1.zip, r-release: catch_1.0.1.zip, r-oldrel: catch_1.0.1.zip
macOS binaries:	r-release (arm64): catch_1.0.1.tgz, r-oldrel (arm64): catch_1.0.1.tgz, r-release (x86_64): catch_1.0.1.tgz, r-oldrel (x86_64): catch_1.0.1.tgz
Old sources:	catch archive

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