TFM: An R package for truncated factor model
The Truncated Factor Model (TFM) is a statistical model for analyzing high-dimensional truncated data, leveraging sparsity and online learning to extract common factors. Its core advantage is efficient modeling of complex data structures with flexible parameter adjustments. We developed an R package...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | SoftwareX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711025001712 |
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| author | Beibei Wu Guangbao Guo |
| author_facet | Beibei Wu Guangbao Guo |
| author_sort | Beibei Wu |
| collection | DOAJ |
| description | The Truncated Factor Model (TFM) is a statistical model for analyzing high-dimensional truncated data, leveraging sparsity and online learning to extract common factors. Its core advantage is efficient modeling of complex data structures with flexible parameter adjustments. We developed an R package named TFM, which integrates methods like SOPC, SPC, PPC, SAPC, IPC, and ttest to compute factor loading and specific variance matrices. These methods were comprehensively evaluated using metrics such as estimation accuracy and mean squared error, demonstrating their effectiveness in handling truncated data. |
| format | Article |
| id | doaj-art-6b2c7c97f2bb451c94e5cada37cceb80 |
| institution | Kabale University |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-6b2c7c97f2bb451c94e5cada37cceb802025-08-20T03:47:33ZengElsevierSoftwareX2352-71102025-09-013110220410.1016/j.softx.2025.102204TFM: An R package for truncated factor modelBeibei Wu0Guangbao Guo1School of Mathematics and Statistics, Shandong University of Technology, Zibo, PR ChinaCorresponding author.; School of Mathematics and Statistics, Shandong University of Technology, Zibo, PR ChinaThe Truncated Factor Model (TFM) is a statistical model for analyzing high-dimensional truncated data, leveraging sparsity and online learning to extract common factors. Its core advantage is efficient modeling of complex data structures with flexible parameter adjustments. We developed an R package named TFM, which integrates methods like SOPC, SPC, PPC, SAPC, IPC, and ttest to compute factor loading and specific variance matrices. These methods were comprehensively evaluated using metrics such as estimation accuracy and mean squared error, demonstrating their effectiveness in handling truncated data.http://www.sciencedirect.com/science/article/pii/S2352711025001712R packageTruncated factor modelSparse online principal component |
| spellingShingle | Beibei Wu Guangbao Guo TFM: An R package for truncated factor model SoftwareX R package Truncated factor model Sparse online principal component |
| title | TFM: An R package for truncated factor model |
| title_full | TFM: An R package for truncated factor model |
| title_fullStr | TFM: An R package for truncated factor model |
| title_full_unstemmed | TFM: An R package for truncated factor model |
| title_short | TFM: An R package for truncated factor model |
| title_sort | tfm an r package for truncated factor model |
| topic | R package Truncated factor model Sparse online principal component |
| url | http://www.sciencedirect.com/science/article/pii/S2352711025001712 |
| work_keys_str_mv | AT beibeiwu tfmanrpackagefortruncatedfactormodel AT guangbaoguo tfmanrpackagefortruncatedfactormodel |