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bigSurvSGD (version 0.0.1)

Big Survival Analysis Using Stochastic Gradient Descent

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) .

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Install

install.packages('bigSurvSGD')

Monthly Downloads

191

Version

0.0.1

License

GPL (>= 2)

Maintainer

Aliasghar Tarkhan

Last Published

October 1st, 2020

Functions in bigSurvSGD (0.0.1)

sparseSurvData

Simulated sparse survival dataset
bigSurvSGD

Big survival data analysis using stochastic gradient descent
oneObsPlugingC

Calculates the gradient and Hessian corresponding to one individual
lambdaMaxC

Calculates the maximum penalty coefficient lambda for which all coefficients become zero
oneChunkC

Updates the coefficients based on one pass of data
survData

Simulated survival dataset