Covariate-Adjusted Tensor Classification in High-Dimensions R package catch version 1.0.1 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. Software Depends: R (>= 3.1.1) Imports: tensr, Matrix, MASS, methods Yuqing Pan <yuqing.pan@stat.fsu.edu> Comprehensive R Archive Network (CRAN) Yuqing Pan <yuqing.pan@stat.fsu.edu>, Qing Mai <mai@stat.fsu.edu>, Xin Zhang <henry@stat.fsu.edu> GPL-2 2021-01-04 application/tgz https://CRAN.R-project.org/package=catch doi:10.32614/CRAN.package.catch