Package: survdnn 1.0.2
survdnn: Deep Neural Networks for Survival Analysis with R 'torch'
Provides deep learning models for right-censored survival data using the 'torch' backend. Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>. The package is described in El Badisy (2026) <doi:10.32614/RJ-2026-008>.
Authors:
survdnn_1.0.2.tar.gz
survdnn_1.0.2.zip(r-4.7-any)survdnn_1.0.2.zip(r-4.6-any)survdnn_1.0.2.zip(r-4.5-any)
survdnn_1.0.2.tgz(r-4.6-any)survdnn_1.0.2.tgz(r-4.5-any)
survdnn_1.0.2.tar.gz(r-4.7-any)survdnn_1.0.2.tar.gz(r-4.6-any)
survdnn_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
survdnn/json (API)
| # Install 'survdnn' in R: |
| install.packages('survdnn', repos = c('https://ielbadisy.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ielbadisy/survdnn/issues
deep-neural-networkssurvival-analysis
Last updated from:0f0e50df9f. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 152 | ||
| source / vignettes | OK | 587 | ||
| linux-release | OK | 549 | ||
| macos-release | OK | 167 | ||
| macos-oldrel | OK | 153 | ||
| windows-devel | OK | 125 | ||
| windows-release | OK | 156 | ||
| windows-oldrel | OK | 119 | ||
| wasm-release | OK | 252 |
Exports:aft_lossbrierbuild_dnncallback_early_stoppingcindex_survmatcox_l2_losscox_losscoxtime_losscv_survdnnevaluate_survdnngridsearch_survdnnibs_survmatplot_losssummarize_cv_survdnnsummarize_tune_survdnnsurvdnntune_survdnn
Dependencies:basetablebitbit64callrclicodetoolscorocpp11descdigestdplyrfarverfurrrfuturegenericsggplot2globalsgluegtableisobandjsonlitelabelinglatticelifecyclelistenvmagrittrMatrixotelparallellypillarpkgconfigprocessxpspurrrR6RColorBrewerRcpprlangrsampleS7safetensorsscalessliderstringistringrsurvivaltibbletidyrtidyselecttorchutf8vctrsviridisLitewarpwithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Brier Score for Right-Censored Survival Data at a Fixed Time | brier |
| Early stopping callback for survdnn | callback_early_stopping |
| Concordance Index from a Survival Probability Matrix | cindex_survmat |
| K-Fold Cross-Validation for survdnn Models | cv_survdnn |
| Evaluate a survdnn Model Using Survival Metrics | evaluate_survdnn |
| Grid Search for survdnn Hyperparameters | gridsearch_survdnn |
| Integrated Brier Score (IBS) from a Survival Probability Matrix | ibs_survmat |
| Plot Training Loss for a survdnn Model | plot_loss |
| Plot survdnn Survival Curves using ggplot2 | plot.survdnn |
| Predict from a survdnn Model | predict.survdnn |
| Print a survdnn Model | print.survdnn |
| Summarize Cross-Validation Results from survdnn | summarize_cv_survdnn |
| Summarize survdnn Tuning Results | summarize_tune_survdnn |
| Summarize a Deep Survival Neural Network Model | summary.survdnn |
| Fit a Deep Neural Network for Survival Analysis | survdnn |
| Tune Hyperparameters for a survdnn Model via Cross-Validation | tune_survdnn |