LFM：一个拉普拉斯因子模型的R包

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

SoftwareX Pub Date : 2025-03-20 DOI:10.1016/j.softx.2025.102133

Siqi Liu, Guangbao Guo

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引用次数: 0

摘要

拉普拉斯因子模型（LFM）是一个有价值的数学工具，用于统计学，机器学习和数据分析。它使用拉普拉斯分布来捕获数据的稀疏性和不确定性，有效地处理复杂的大规模数据。提出的R包称为LFM，它具有基于拉普拉斯分布构建因子模型的能力，并且可以通过灵活调整拉普拉斯分布的参数来定制模型的构建。此外，LFM包集成了各种技术，包括稀疏在线主成分（SOPC），增量主成分（IPC），投影主成分（PPC），随机近似主成分（SAPC），稀疏主成分（SPC），以及其他PC方法和农场测试方法。通过对参数估计精度、均方误差、稀疏度等指标的评价，验证了这些方法在拉普拉斯因子模型中的有效性和实用性。

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LFM: An R package for laplace factor model

The Laplace Factor Model (LFM) is a valuable mathematical tool used in statistics, machine learning, and data analysis. It uses the Laplace distribution to capture data sparsity and uncertainty, effectively handling complex, large-scale data. The proposed R package, called LFM, has the capability to construct factor models based on the Laplace distribution, and it allows for customized model building by flexibly adjusting the parameters of the Laplace distribution. Additionally, the LFM package integrates various techniques including Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Projection Principal Component (PPC), Stochastic Approximate Principal Component (SAPC), Sparse Principal Component (SPC), and other PC methods and the Farm Test method. By evaluating indicators such as the accuracy of parameter estimation, mean square error, and sparsity, this study verifies the effectiveness and practicality of these methods in the Laplace Factor Model.

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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.