Package: binequality
Type: Package
Title: Methods for Analyzing Binned Income Data
Version: 1.0.4
Date: 2018-11-05
Author: Samuel V. Scarpino, Paul von Hippel, and Igor Holas
Maintainer: Samuel V. Scarpino <s.scarpino@northeastern.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)
NeedsCompilation: no
Packaged: 2018-11-05 14:01:07 UTC; scarpino
Repository: CRAN
Date/Publication: 2018-11-05 14:20:03 UTC
Built: R 4.4.0; ; 2024-05-31 14:16:55 UTC; unix
