Package: catch 1.0.1

Yuqing Pan

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) <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.

Authors:Yuqing Pan <[email protected]>, Qing Mai <[email protected]>, Xin Zhang <[email protected]>

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catch/json (API)

# Install 'catch' in R:
install.packages('catch', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • fortran– Runtime library for GNU Fortran applications
Datasets:
  • csa - Colorimetric sensor array (CSA) data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

fortran

1.43 score 27 scripts 160 downloads 5 exports 5 dependencies

Last updated 4 years agofrom:6bf45e82ea. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 27 2024
R-4.5-linux-x86_64OKDec 27 2024

Exports:adjtencatchcatch_matrixcv.catchpredict.catch

Dependencies:assertthatlatticeMASSMatrixtensr