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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.
| Version: | 1.0.4 |
| 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) |
| Published: | 2018-11-05 |
| Author: | Samuel V. Scarpino, Paul von Hippel, and Igor Holas |
| Maintainer: | Samuel V. Scarpino <s.scarpino at northeastern.edu> |
| License: | GPL (≥ 3.0) |
| NeedsCompilation: | no |
| Citation: | binequality citation info |
| CRAN checks: | binequality results |
| Reference manual: | binequality.pdf |
| Package source: | binequality_1.0.4.tar.gz |
| Windows binaries: | r-devel: binequality_1.0.4.zip, r-release: binequality_1.0.4.zip, r-oldrel: binequality_1.0.4.zip |
| macOS binaries: | r-release (arm64): binequality_1.0.4.tgz, r-oldrel (arm64): binequality_1.0.4.tgz, r-release (x86_64): binequality_1.0.4.tgz, r-oldrel (x86_64): binequality_1.0.4.tgz |
| Old sources: | binequality archive |
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