survdnn: Deep Neural Networks for Survival Analysis Using '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>.
| Version: |
0.6.3 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
torch, survival, stats, utils, tibble, dplyr, purrr, tidyr, ggplot2, methods, rsample, cli, glue |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: |
2025-10-30 |
| DOI: |
10.32614/CRAN.package.survdnn |
| Author: |
Imad EL BADISY [aut, cre] |
| Maintainer: |
Imad EL BADISY <elbadisyimad at gmail.com> |
| BugReports: |
https://github.com/ielbadisy/survdnn/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/ielbadisy/survdnn |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
survdnn results |
Documentation:
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