spar: Sparse Projected Averaged Regression in R
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866910717777543168 |
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| author | Parzer, Roman Vana-Gür, Laura Filzmoser, Peter |
| author_facet | Parzer, Roman Vana-Gür, Laura Filzmoser, Peter |
| contents | Package spar for R builds ensembles of predictive generalized linear models with high-dimensional predictors. It employs an algorithm utilizing variable screening and random projection tools to efficiently handle the computational challenges associated with large sets of predictors. The package is designed with a strong focus on extensibility. Screening and random projection techniques are implemented as S3 classes with user-friendly constructor functions, enabling users to easily integrate and develop new procedures. This design enhances the package's adaptability and makes it a powerful tool for a variety of high-dimensional applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17808 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | spar: Sparse Projected Averaged Regression in R Parzer, Roman Vana-Gür, Laura Filzmoser, Peter Computation Methodology Package spar for R builds ensembles of predictive generalized linear models with high-dimensional predictors. It employs an algorithm utilizing variable screening and random projection tools to efficiently handle the computational challenges associated with large sets of predictors. The package is designed with a strong focus on extensibility. Screening and random projection techniques are implemented as S3 classes with user-friendly constructor functions, enabling users to easily integrate and develop new procedures. This design enhances the package's adaptability and makes it a powerful tool for a variety of high-dimensional applications. |
| title | spar: Sparse Projected Averaged Regression in R |
| topic | Computation Methodology |
| url | https://arxiv.org/abs/2411.17808 |