Package: bigSurvSGD 1.0.0

bigSurvSGD: Big Survival Analysis Using Stochastic Gradient Descent

Fits Cox Model via stochastic gradient descent (SGD). 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.

Authors:Aliasghar Tarkhan [aut, cre], Noah Simon [aut]

bigSurvSGD_1.0.0.tar.gz
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bigSurvSGD_1.0.0.tgz(r-4.6-x86_64)bigSurvSGD_1.0.0.tgz(r-4.6-arm64)bigSurvSGD_1.0.0.tgz(r-4.5-x86_64)bigSurvSGD_1.0.0.tgz(r-4.5-arm64)
bigSurvSGD_1.0.0.tar.gz(r-4.7-arm64)bigSurvSGD_1.0.0.tar.gz(r-4.7-x86_64)bigSurvSGD_1.0.0.tar.gz(r-4.6-arm64)bigSurvSGD_1.0.0.tar.gz(r-4.6-x86_64)
bigSurvSGD_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
bigSurvSGD/json (API)

# Install 'bigSurvSGD' in R:

install.packages('bigSurvSGD', repos = c('https://atarkhan.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/atarkhan/bigsurvsgd/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

sparseSurvData - Simulated sparse survival dataset
survData - Simulated survival dataset

On CRAN:

cpp

3.43 score 9 stars 1 packages 1 scripts 346 downloads 6 exports 12 dependencies

Last updated from:8dc4e4aa4b. Checks:13 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	147
linux-devel-x86_64	NOTE	154
source / vignettes	OK	188
linux-release-arm64	NOTE	139
linux-release-x86_64	NOTE	137
macos-release-arm64	NOTE	154
macos-release-x86_64	NOTE	213
macos-oldrel-arm64	NOTE	141
macos-oldrel-x86_64	NOTE	231
windows-devel-arm64	NOTE	362
windows-devel-x86_64	NOTE	137
windows-release-arm64	NOTE	373
windows-release-x86_64	NOTE	88
windows-oldrel-x86_64	NOTE	129
wasm-release	OK	124

Exports:bigSurvSGD lambdaMaxC oneChunkC oneObsPlugingC plot.bigSurvSGD print.bigSurvSGD

Dependencies:BH bigmemory bigmemory.sri codetools doParallel foreach iterators lattice Matrix Rcpp survival uuid

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Big survival data analysis using stochastic gradient descent	bigSurvSGD plot.bigSurvSGD print.bigSurvSGD
Calculates the maximum penalty coefficient lambda for which all coefficients become zero	lambdaMaxC
Updates the coefficients based on one pass of data	oneChunkC
Calculates the gradient and Hessian corresponding to one individual	oneObsPlugingC
Simulated sparse survival dataset	sparseSurvData
Simulated survival dataset	survData