Description: Functions for analyzing check-all-that-apply (CATA) data from consumer and sensory tests. Cochran's Q test, McNemar's test, and Penalty-Lift analysis provided, as described in for CATA data analysis by Meyners, Castura & Carr (2013) <doi:10.1016/j.foodqual.2013.06.010>. Cluster analysis can be performed using b-cluster analysis. The quality of cluster analysis solutions can be evaluated using various measures. The methods related to b-cluster analysis are described in a manuscript by Castura, Meyners, Varela & Naes (2022) <doi:10.1016/j.foodqual.2022.104564>.

Title: Access the 'Spark Catalog' API via 'sparklyr'
Authors@R: person("Nathan", "Eastwood", "", "nathan.eastwood@icloud.com",
Description: Gain access to the 'Spark Catalog' API making use of the 'sparklyr' API. 'Catalog' <https://spark.apache.org/docs/2.4.3/api/java/org/apache/spark/sql/catalog/Catalog.html> is the interface for managing a metastore (aka metadata catalog) of relational entities (e.g. database(s), tables, functions, table columns and temporary views).
Suggests: DBI, knitr, pkgdown, rmarkdown, roxygen2, tinytest

Suggests: DBI, knitr, rmarkdown, roxygen2, tinytest

Title: Covariate-Adjusted Tensor Classification in High-Dimensions
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) <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.