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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711025001001 |
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| Summary: | 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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| ISSN: | 2352-7110 |