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: 2026-07-12 05:06:08 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2018-11-05 13:20:03 UTC
RemoteUrl: https://github.com/cran/binequality
RemoteRef: HEAD
RemoteSha: a3398c123ed494309f78ddc0251f228262345078