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bigPLScox: Partial Least Squares for Cox Models with Big Matrices
Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models for big data. Provides a Partial Least Squares (PLS) algorithm adapted to Cox proportional hazards models that works with 'bigmemory' matrices without loading the entire dataset in memory. Also implements a gradient-descent based solver for Cox proportional hazards models that works directly on 'bigmemory' matrices. Bertrand and Maumy (2023) <https://hal.science/hal-05352069>, and <https://hal.science/hal-05352061> highlighted fitting and cross-validating PLS-based Cox models to censored big data.
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Package details |
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| Author | Frederic Bertrand [cre, aut] (ORCID: <https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (ORCID: <https://orcid.org/0000-0002-4615-1512>) |
| Maintainer | Frederic Bertrand <frederic.bertrand@lecnam.net> |
| License | GPL-3 |
| Version | 0.8.1 |
| URL | https://fbertran.github.io/bigPLScox/ https://github.com/fbertran/bigPLScox |
| Package repository | View on CRAN |
| Installation |
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