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.
|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)|
|Author:||Samuel V. Scarpino, Paul von Hippel, and Igor Holas|
|Maintainer:||Samuel V. Scarpino <scarpino at utexas.edu>|
|License:||GPL (≥ 3.0)|
|Citation:||binequality citation info|
|CRAN checks:||binequality results|
|Windows binaries:||r-devel: binequality_1.0.1.zip, r-release: binequality_1.0.1.zip, r-oldrel: binequality_1.0.1.zip|
|OS X Mavericks binaries:||r-release: binequality_1.0.1.tgz, r-oldrel: binequality_1.0.1.tgz|
|Old sources:||binequality archive|
Please use the canonical form https://CRAN.R-project.org/package=binequality to link to this page.