binequality : Methods for Analyzing Binned Income Data

Package: binequality
Type: Package
Title: Methods for Analyzing Binned Income Data
Version: 0.6.1
Date: 2014-09-14
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.
License: GPL (>= 3.0)
LazyLoad: yes
Depends: R (>= 2.10), gamlss (>= 4.2.7), gamlss.cens (>= 4.2.7),
gamlss.dist (>= 4.3.0)
Imports: survival (>= 2.37-7), ineq (>= 0.2-11)
Packaged: 2014-09-14 22:50:02 UTC; scarpino
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-15 07:27:50