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).

Access public spatial data available under the 'INSPIRE'
    directive. Tools for downloading references and addresses of
    properties, as well as map images.

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.

Methods and utilities for testing, identifying, selecting and  
    mutating objects as categorical or continous types. These functions work on both 
    atomic vectors as well as recursive objects: data.frames, data.tables, 
    tibbles, lists, etc..

Categorical data analysis by AIC. The methodology is described in
 Sakamoto (1992) <ISBN 978-0-7923-1429-5>.

This R-package contains examples from the book "Regression for Categorical Data", Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.

Datasets used in the book "Categorical Data Analysis"
   by Agresti (2012, ISBN:978-0-470-46363-5) but not printed in the book.
   Datasets and help pages were automatically produced from the source
   <https://users.stat.ufl.edu/~aa/cda/data.html> by the R script foo.R,
   which can be found in the GitHub repository.