bigSurvSGD: Big Survival Analysis Using Stochastic Gradient Descent

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
DOI:	10.32614/CRAN.package.bigSurvSGD
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

Documentation:

Reference manual:

bigSurvSGD.html , bigSurvSGD.pdf

Downloads:

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

Reverse dependencies:

Reverse imports:

bigPLScox

Linking:

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