Deep Neural Networks for Survival Analysis with R 'torch'
Brier Score for Right-Censored Survival Data at a Fixed Time
Build a Deep Neural Network for Survival Analysis
Early stopping callback for survdnn
Concordance Index from a Survival Probability Matrix
K-Fold Cross-Validation for survdnn Models
Evaluate a survdnn Model Using Survival Metrics
Grid Search for survdnn Hyperparameters
Integrated Brier Score (IBS) from a Survival Probability Matrix
Plot Training Loss for a survdnn Model
Plot survdnn Survival Curves using ggplot2
Predict from a survdnn Model
Print a survdnn Model
Summarize Cross-Validation Results from survdnn
Summarize survdnn Tuning Results
Summarize a Deep Survival Neural Network Model
Loss Functions for survdnn Models
Fit a Deep Neural Network for Survival Analysis
Tune Hyperparameters for a survdnn Model via Cross-Validation
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>.