Underlying support functions for RcmdrPlugin.BCA and a
companion to the book Customer and Business Analytics: Applied
Data Mining for Business Decision Making Using R by Daniel S.
Putler and Robert E. Krider

RcmdrMisc (>= 1.0-1), Rcmdr (>= 2.1-0), car (>= 2.0-21),
rpart, flexclust, clv

Block Coordinate Ascent with One-Step Generalized Rosen
Algorithm
Wang Yudong, Ye Zhisheng, and Cao Hongyuan
Implementing the Block Coordinate Ascent with One-Step Generalized Rosen (BCA1SG) algorithm on the semiparametric models for panel count data, interval-censored survival data, and degradation data. A comprehensive description of the BCA1SG algorithm can be found in Wang et al. (2020) <https://github.com/yudongstat/BCA1SG/blob/master/BCA1SG.pdf>. For details of the semiparametric models for panel count data, interval-censored survival data, and degradation data, please see Wellner and Zhang (2007) <doi:10.1214/009053607000000181>, Huang and Wellner (1997) <ISBN:978-0-387-94992-5>, and Wang and Xu (2010) <doi:10.1198/TECH.2009.08197>, respectively.

Bias-corrected Bayesian Classification with Selected Features
This software is used to predict the discrete class labels
based on a selected subset of high-dimensional features, such
as expression levels of genes. The data are modeled with a
hierarchical Bayesian models using heavy-tailed t distributions
as priors. When a large number of features are available, one
may like to select only a subset of features to use, typically
those features strongly correlated with the response in
training cases. Such a feature selection procedure is however
invalid since the relationship between the response and the
features has be exaggerated by feature selection. This package
provides a way to avoid this bias and yield better-calibrated
predictions for future cases when one uses F-statistic to
select features.

This package is used to predict the discrete class labels
based on a selected subset of high-dimensional features, such
as expression levels of genes. The data are modeled with a
hierarchical Bayesian models using heavy-tailed t distributions
as priors. When a large number of features are available, one
may like to select only a subset of features to use, typically
those features strongly correlated with the response in
training cases. Such a feature selection procedure is however
invalid since the relationship between the response and the
features has be exaggerated by feature selection. This package
provides a way to avoid this bias and yield better-calibrated
predictions for future cases when one uses F-statistic to
select features.