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Fits Cox model via stochastic gradient descent. This implementation avoids computational instability of the standard Cox Model when dealing large datasets. Furthermore, it scales up with large datasets that do not fit the memory. It also handles large sparse datasets using proximal stochastic gradient descent algorithm. For more details about the method, please see Aliasghar Tarkhan and Noah Simon (2020) <doi:10.48550/arXiv.2003.00116>.
| Version: | 0.0.1 |
| Depends: | foreach, parallel, R (≥ 3.5.0) |
| Imports: | Rcpp (≥ 1.0.4), bigmemory, doParallel, survival |
| LinkingTo: | Rcpp |
| Published: | 2020-10-01 |
| Author: | Aliasghar Tarkhan [aut, cre], Noah Simon [aut] |
| Maintainer: | Aliasghar Tarkhan <atarkhan at uw.edu> |
| BugReports: | https://github.com/atarkhan/bigSurvSGD/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | bigSurvSGD results |
| Reference manual: | bigSurvSGD.pdf |
| Package source: | bigSurvSGD_0.0.1.tar.gz |
| Windows binaries: | r-devel: bigSurvSGD_0.0.1.zip, r-release: bigSurvSGD_0.0.1.zip, r-oldrel: bigSurvSGD_0.0.1.zip |
| macOS binaries: | r-release (arm64): bigSurvSGD_0.0.1.tgz, r-oldrel (arm64): bigSurvSGD_0.0.1.tgz, r-release (x86_64): bigSurvSGD_0.0.1.tgz, r-oldrel (x86_64): bigSurvSGD_0.0.1.tgz |
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