survdnn

survdnn 0.7.6

CRAN resubmission update

• Hardened \\donttest{} examples that train models by guarding them with torch::torch_is_installed() so checks pass on systems without Torch.
• Updated examples to load the veteran dataset explicitly via survival::veteran (instead of data(veteran, ...)).
• Added explicit verbose support to cv_survdnn() and tune_survdnn(), with clearer and consistent progress messages across fit/cv/tune workflows.
• Added optional .threads argument to survdnn(), cv_survdnn(), and tune_survdnn() to control Torch CPU thread count explicitly when needed.
• Regenerated documentation (man/*.Rd) to reflect example updates.
• Removed internal-tool references from release notes and submission notes.

survdnn 0.7.5

Main changes

• Fixed and stabilized loss implementations for AFT and CoxTime models, ensuring correct handling of time scaling, parameter learning, and numerical stability.
• Corrected and harmonized prediction methods (predict.survdnn) across all supported losses (Cox, Cox L2, AFT, CoxTime), including survival and risk predictions.
• Improved internal consistency checks to ensure valid survival probabilities (monotonicity and bounds).
• Updated and expanded unit tests to cover prediction behavior, edge cases, and reproducibility.
• Regenerated Rd documentation to fully document all function arguments and remove previous documentation warnings.

survdnn 0.7.0

Major changes

• Added full support for training control mechanisms, including early stopping callbacks and complete loss tracking across epochs.

• Introduced plot_loss() to visualize training loss trajectories and diagnose convergence or instability.

• Centralized reproducibility control via the .seed argument in survdnn(), synchronizing both R and Torch random number generators.

• Expanded optimizer support to include Adam, AdamW, SGD, RMSprop, and Adagrad, with customizable optimizer arguments.

• Enhanced prediction methods to robustly support linear predictors, survival probabilities, and cumulative risk across all supported loss functions.

• Added explicit and user-controllable missing-data handling (na_action = "omit" or "fail"), with informative messages.

Minor changes

• Improved handling of formulas using Surv(...) ~ . in prediction and evaluation.

• Improved printing and summary methods for fitted survdnn objects.

• Expanded unit test coverage, including optimizers, plotting utilities, and missing-data edge cases.

Bug fixes

• Fixed inconsistencies in prediction and evaluation when formulas used . expansion.

survdnn 0.6.2

Maintenance release (CRAN compliance)

• Removed automatic torch::install_torch() on load:

  The package no longer downloads or installs Torch libraries automatically when loaded. The .onLoad() function now performs only a silent availability check, and .onAttach() displays an informative message instructing users to manually run torch::install_torch() when necessary.

  This update ensures full compliance with CRAN policies that forbid modification of user environments or network activity during package load.

• Updated startup messages for clearer user guidance.

• Internal documentation updates and version bump for CRAN resubmission.

survdnn 0.6.1

Infrastructure and testing improvements

• Added conditional test skipping: tests and examples now use skip_if_not(torch_is_installed()) and skip_on_cran() to avoid failures on systems where Torch is not available (thanks to @dfalbel for the PR).

• Regenerated documentation (RoxygenNote: 7.3.3) and updated man pages.

• Minor internal consistency fixes and CI check updates.

survdnn 0.6.0

First public release of survdnn.

Features

• survdnn(): Fit deep learning survival models using a formula interface.
• Supported loss functions:
  • Cox partial likelihood ("cox")
  • L2-penalized Cox ("cox_l2")
  • Time-dependent Cox ("coxtime")
  • Accelerated Failure Time ("aft")
• Cross-validation via cv_survdnn().
• Hyperparameter tuning with tune_survdnn().
• Survival probability prediction and curve plotting.
• Evaluation metrics: Concordance index (C-index), Brier score, and Integrated Brier Score (IBS).

CRAN submission prepared, including README, documentation, and automated tests.