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gk: An R Package for the g-and-k and generalised g-and-h Distributions

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arxiv 1706.06889 v1 pith:KMNAORCJ submitted 2017-06-21 stat.CO

Dennis Prangle This is my paper · ORCID
classification stat.CO
keywords distributionsdatadistributionfunctiong-and-hg-and-kgeneralisedinference
verification ladder T0 review T1 audit T2 compute T3 formal
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The g-and-k and (generalised) g-and-h distributions are flexible univariate distributions which can model highly skewed or heavy tailed data through only four parameters: location and scale, and two shape parameters influencing the skewness and kurtosis. These distributions have the unusual property that they are defined through their quantile function (inverse cumulative distribution function) and their density is unavailable in closed form, which makes parameter inference complicated. This paper presents the gk R package to work with these distributions. It provides the usual distribution functions and several algorithms for inference of independent identically distributed data, including the finite difference stochastic approximation method, which has not been used before for this problem.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Robust Simulation Based Inference Through Robust Optimal Transport

    stat.ME 2026-05 unverdicted novelty 6.0 of 10

    Proposes a KL-informed robust optimal transport divergence with stochastic estimation and bootstrap-based SBI for robust inference under joint geometric and TV contamination.

  2. Robust Simulation Based Inference

    stat.ME 2025-08 unverdicted novelty 6.0 of 10

    A robust SBI framework that provides valid frequentist inference under model misspecification by targeting projection parameters and expanding models through exponential tilting.

  3. SoK: Stablecoins for Digital Transformation -- Design, Metrics, and Application with Real World Asset Tokenization as a Case Study

    econ.GN 2025-08 unverdicted novelty 4.0 of 10

    The paper presents a taxonomy, stakeholder-oriented evaluation framework, and an open-source benchmarking pipeline for stablecoin systems, illustrated with a real-world asset tokenization case study.

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