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catch  

Covariate-Adjusted Tensor Classification in High-Dimensions
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Download and install catch package within the R console
Install from CRAN:
install.packages("catch")

Install from Github:
library("remotes")
install_github("cran/catch")

Install by package version:
library("remotes")
install_version("catch", "1.0.1")



Attach the package and use:
library("catch")
Maintained by
Yuqing Pan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-05-14
Latest Update: 2021-01-04
Description:
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) . 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.
How to cite:
Yuqing Pan (2018). catch: Covariate-Adjusted Tensor Classification in High-Dimensions. R package version 1.0.1, https://cran.r-project.org/web/packages/catch. Accessed 04 Jun. 2026.
Previous versions and publish date:
1.0 (2018-05-14 15:42)
Other packages that cited catch R package
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Other R packages that catch depends, imports, suggests or enhances
Complete documentation for catch
Functions, R codes and Examples using the catch R package
Some associated functions: adjten . catch . catch_matrix . csa . cvcatch . predictcatch . 
Some associated R codes: adjust_predict.R . adjusttensor.R . catch.R . catch_matrix.R . cv.catch.R . tensor_main.R . tensor_predict.R . tensor_utility.R .  Full catch package functions and examples
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