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bigSurvSGD  

Big Survival Analysis Using Stochastic Gradient Descent
View on CRAN: Click here


Download and install bigSurvSGD package within the R console
Install from CRAN:
install.packages("bigSurvSGD")

Install from Github:
library("remotes")
install_github("cran/bigSurvSGD")

Install by package version:
library("remotes")
install_version("bigSurvSGD", "0.0.1")



Attach the package and use:
library("bigSurvSGD")
Maintained by
Aliasghar Tarkhan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-10-01
Latest Update: 2020-10-01
Description:
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) .
How to cite:
Aliasghar Tarkhan (2020). bigSurvSGD: Big Survival Analysis Using Stochastic Gradient Descent. R package version 0.0.1, https://cran.r-project.org/web/packages/bigSurvSGD. Accessed 27 Jul. 2026.
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Complete documentation for bigSurvSGD
Functions, R codes and Examples using the bigSurvSGD R package
Some associated functions: bigSurvSGD . lambdaMaxC . oneChunkC . oneObsPlugingC . sparseSurvData . survData . 
Some associated R codes: RcppExports.R . bigSurvSGD.R . data-sparseSurvData.R . data-survData.R .  Full bigSurvSGD package functions and examples
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