beta4dist: A Python package for the four-parameter Beta distribution and likelihood-based estimation

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2025-07-31 DOI:10.1016/j.softx.2025.102273

Soham Ghosh , Sujay Mukhoti , Abhirup Banerjee

引用次数: 0

Abstract

We present beta4dist, the first open-source Python package that implements a likelihood-based estimation framework for the four-parameter Beta distribution. This flexible distribution is widely used to model bounded, continuous data with diverse shapes, including skewed and heavy-tailed patterns. Such datasets are common in fields such as hydrology, environmental science, and reliability engineering. The software estimates location parameters via order statistics and computes shape parameters using marginal likelihood optimization, ensuring that all estimates adhere to natural parameter constraints. In addition to core estimation routines, beta4dist includes utilities for density evaluation, random sampling, cumulative distribution, quantiles, and model diagnostics. The package is fully tested, easy to integrate into standard Python workflows, and supports both research reproducibility and practical applications requiring shape-robust modeling tools.

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beta4dist：用于四参数Beta分布和基于似然估计的Python包

我们提出了beta4dist，这是第一个为四参数Beta分布实现基于似然估计框架的开源Python包。这种灵活的分布被广泛用于建模具有不同形状的有界连续数据，包括倾斜和重尾模式。这些数据集在水文学、环境科学和可靠性工程等领域很常见。该软件通过顺序统计估计位置参数，并使用边际似然优化计算形状参数，确保所有估计都符合自然参数约束。除了核心估计例程之外，beta4dist还包括用于密度评估、随机抽样、累积分布、分位数和模型诊断的实用程序。该软件包经过全面测试，易于集成到标准Python工作流中，并支持研究可重复性和需要形状健壮的建模工具的实际应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

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