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Changelog

Source: NEWS.md

inequality 0.2.1

Patch release. One fix, to a side effect rather than to any returned value.

iq_sample_data() called set.seed(42L) and did not restore the previous state, so calling it reseeded the caller’s random stream. Anyone who ran iq_sample_data() to get a demonstration data frame found every subsequent random draw in their session silently reset to a fixed sequence, which quietly breaks reproducibility for the analysis around it. The seed now applies for the duration of the call only and .Random.seed is restored on exit.

The generated data is unchanged: a given type still returns exactly the same values, and now does so regardless of the caller’s own seed. No inequality measure returns a different number as a result of this release.

inequality 0.2.0

This release responds to feedback from Frank Cowell and Emmanuel Flachaire (personal communication, 1 May 2026) on the v0.1.0 release. Two gaps were flagged: confidence intervals were available only for the Gini, and the package rejected non-positive values for the Gini and the top shares. A follow-up internal audit produced several smaller fixes that ship together.

Confidence intervals on every inequality measure

Bootstrap confidence intervals are now available on every inequality function via ci = TRUE. Each function gains ci, R, and level arguments matching the existing Gini API. Results are returned in ci_lower, ci_upper, and se fields on the output object and shown by the print method.

Functions extended: iq_theil(), iq_atkinson(), iq_sgini(), iq_palma(), iq_hoover(), iq_kolm(), iq_percentile_ratio(), iq_polarisation(), iq_shares(), iq_concentration(), iq_kakwani(), and iq_poverty().

The bootstrap uses probability-proportional resampling, so survey weights flow through to the variance, not just the point estimate.

iq_compare() runs one bootstrap loop, propagates CIs to every row

When ci = TRUE, iq_compare() now runs a single resample loop and attaches ci_lower and ci_upper columns to every row of the table. The old gini_ci field is removed. The table now also covers S-Gini, Kolm, and Wolfson (12 measures, up from 9).

Negative values are now supported via negatives = "keep"

Functions that are mathematically defined for distributions containing negative values now accept negatives = c("error", "keep"), with "error" as the default for back-compatibility. With negatives = "keep":

  • iq_gini() and iq_sgini() permit negatives; the index is still computed by the standard formula but is no longer bounded in [0, 1]. The print method emits a note when the input contains negatives.
  • iq_shares() permits negatives; segment shares may fall outside [0, 1] and a warning is issued. If total income is non-positive, the function returns NA shares with a warning.
  • iq_palma(), iq_hoover(), and iq_polarisation() similarly accept negatives.

iq_kolm() already worked for negative values and is unchanged.

iq_atkinson() and iq_theil() continue to require strictly positive values: they involve log(x) or x^(1 - epsilon) for which the formula is mathematically undefined at zero or below. The error message now documents this explicitly.

Bug fixes

  • iq_kakwani() no longer takes the absolute value of post-tax income before computing the post-tax Gini. Households whose post-tax income is negative are now reflected honestly in the Reynolds-Smolensky index.
  • iq_palma() and iq_polarisation() now warn rather than abort when the relevant denominator is non-positive (returning NA).
  • The standard Gini now returns NA (with a warning) when the population mean is non-positive. Previously the function returned 0 when mu == 0, which conflated “perfect equality” with “undefined”. With negatives = "keep" set, the user is pointed at normalised = TRUE for the Raffinetti et al. (2017) bounded variant.
  • The Watts poverty index drops observations with x = 0 from the Watts sum (since log(line / 0) diverges) and emits a one-time warning. FGT measures and the Sen index continue to include all poor observations.
  • iq_percentile_ratio() now warns when the lower percentile is negative, since the resulting ratio sign-flips and has no inequality interpretation.
  • The error message for measures that require strictly positive input (Theil, Atkinson, decompose, growth_incidence) no longer suggests setting negatives = "keep", which those wrappers do not expose. Instead the message points the user at measures that admit zero or negative support.

New features (audit follow-up)

  • iq_gini() gains a normalised flag implementing the Raffinetti, Siletti and Vernizzi (2017) Gini variant, which is bounded in [-1, 1] for distributions containing negative values.
  • iq_compare() gains a negatives argument. With negatives = "keep" it permits zero or negative input and returns NA for the Theil and Atkinson rows (which are mathematically undefined for non-positive values), while still computing Gini, S-Gini, Kolm, Wolfson, Hoover, Palma and percentile-ratio rows.
  • iq_concentration() gains a correction = "wagstaff" option for the Wagstaff (2005) normalised concentration index, alongside the existing Erreygers (2009) correction.
  • ?iq_theil now documents the convention difference vs the legacy ineq package (ineq::Theil(x, parameter = 0) corresponds to GE(1) / Theil T here, not to GE(0) / Theil L).
  • New tests/testthat/test-axioms.R locks in scale invariance, Kolm translation invariance, Pigou-Dalton transfer principle, anonymity, parameter monotonicity, decomposition exactness, the Erreygers and Wagstaff bounds, and bootstrap nominal coverage.
  • New tests/testthat/test-cross-package.R cross-checks Gini, Theil, and Atkinson values against the legacy ineq package on a fixed seed. Skipped when ineq is not installed and on CRAN.

Acknowledgements

Thanks to Frank Cowell and Emmanuel Flachaire for the careful read and the two-line list of gaps.

inequality 0.1.0

CRAN release: 2026-04-20

  • Initial release.

Inequality indices

  • Gini coefficient with bootstrap or asymptotic (jackknife) confidence intervals via iq_gini(), following Davidson (2009).
  • Extended S-Gini family with adjustable inequality aversion parameter via iq_sgini(), following Donaldson and Weymark (1980).
  • Theil T (GE(1)), mean log deviation (GE(0)), and general GE(alpha) via iq_theil(), following Theil (1967) and Shorrocks (1980).
  • Atkinson index with inequality aversion parameter via iq_atkinson().
  • Kolm absolute inequality index via iq_kolm().
  • Palma ratio (top 10% / bottom 40% income shares) via iq_palma().
  • Hoover index (Robin Hood / Pietra index) via iq_hoover().
  • Percentile ratios (P90/P10, P80/P20, custom) via iq_percentile_ratio().

Distribution and decomposition

  • Lorenz curve with base graphics plot method via iq_lorenz().
  • Between-within group decomposition for the generalised entropy family via iq_decompose(), following Bourguignon (1979).
  • Income share tabulation (bottom 50%, middle 40%, top 10%, top 1%) via iq_shares().
  • Concentration index for health inequality with optional Erreygers (2009) correction via iq_concentration().
  • Wolfson bipolarisation index via iq_polarisation().

Poverty

  • Foster-Greer-Thorbecke poverty measures (headcount, gap, severity), Sen index, and Watts index via iq_poverty().
  • Growth incidence curve with plot method via iq_growth_incidence(), following Ravallion and Chen (2003).

Fiscal

  • Kakwani progressivity index and Reynolds-Smolensky redistribution index via iq_kakwani().

Utilities

  • Side-by-side comparison of all major indices via iq_compare().
  • All functions accept optional survey weights.