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:
bigSurvSGD_1.0.0.tar.gz
bigSurvSGD_1.0.0.zip(r-4.5)bigSurvSGD_1.0.0.zip(r-4.4)bigSurvSGD_1.0.0.zip(r-4.3)
bigSurvSGD_1.0.0.tgz(r-4.4-x86_64)bigSurvSGD_1.0.0.tgz(r-4.4-arm64)bigSurvSGD_1.0.0.tgz(r-4.3-x86_64)bigSurvSGD_1.0.0.tgz(r-4.3-arm64)
bigSurvSGD_1.0.0.tar.gz(r-4.5-noble)bigSurvSGD_1.0.0.tar.gz(r-4.4-noble)
bigSurvSGD_1.0.0.tgz(r-4.4-emscripten)bigSurvSGD_1.0.0.tgz(r-4.3-emscripten)
bigSurvSGD.pdf |bigSurvSGD.html✨
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
- sparseSurvData - Simulated sparse survival dataset
- survData - Simulated survival dataset
Last updated 4 years agofrom:8dc4e4aa4b. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 06 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 06 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 06 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 06 2024 |
| R-4.4-mac-x86_64 | NOTE | Nov 06 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 06 2024 |
| R-4.3-win-x86_64 | NOTE | Nov 06 2024 |
| R-4.3-mac-x86_64 | NOTE | Nov 06 2024 |
| R-4.3-mac-aarch64 | NOTE | Nov 06 2024 |
Exports:bigSurvSGDlambdaMaxConeChunkConeObsPlugingCplot.bigSurvSGDprint.bigSurvSGD
Dependencies:BHbigmemorybigmemory.sricodetoolsdoParallelforeachiteratorslatticeMatrixRcppsurvivaluuid
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 |
