beta4dist：用于四参数Beta分布和基于似然估计的Python包

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

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引用次数: 0

摘要

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。

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

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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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

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.