Author: Dirk Eddelbuettel, Ista Zahn and Rob Hyndman
theme by Matthias Vogelgesang and others (now included in 'TeXLive'), the
'IQSS' by Ista Zahn (which is included here), and the 'Monash' theme by
Rob J Hyndman. Additional (free) fonts may be needed: 'Metropolis' prefers
'Fira', and 'IQSS' requires 'Libertinus'.

Title: Multi-Class Discriminant Analysis using Binary Predictors
Author: Sebastian Gibb and Korbinian Strimmer.
Description: The "binda" package implements functions for multi-class
discriminant analysis using binary predictors, for corresponding
variable selection, and for dichotomizing continuous data.

Description: Implements functions for multi-class

Title: Generation of Artificial Binary Data
Author: Friedrich Leisch <Friedrich.Leisch@ci.tuwien.ac.at> and
Description: Generation of correlated artificial binary data.
Packaged: Sat Jan 31 13:30:58 2004; hornik

Packaged: Wed Sep 15 14:28:58 2004; hornik

Packaged: Fri Feb 25 14:37:48 2005; leisch

Depends: R (>= 2.0.0), e1071, mvtnorm, adapt
Packaged: Wed Oct  4 12:14:31 2006; hornik

Author: Friedrich Leisch and Andreas Weingessel
Packaged: Mon Oct  9 18:38:42 2006; leisch

Author: Friedrich Leisch and Andreas Weingessel and Kurt Hornik

Authors@R: c(person("Friedrich", "Leisch", role = c("aut", "cre"),
person("Andreas", "Weingessel", role = "aut"),

Description: Provides a simple interface for creating active bindings where the
bound function accepts additional arguments.

Title: An 'Rcpp' Interface to Active Bindings
Description: Provides an easy way to fill an environment with active bindings

Title: Methods for Analyzing Binned Income Data
Author: Samuel V. Scarpino, Paul von Hippel, and Igor Holas
Maintainer: Samuel V. Scarpino <scarpino@utexas.edu>
Description: Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored.  We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results.  We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.