Changes in version 0.7.6 (2026-04-29)                  

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.

                 Changes in version 0.7.5 (2026-01-08)                  

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.

                 Changes in version 0.7.0 (2025-12-23)                  

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.

                        Changes in version 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.

                        Changes in version 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.

                 Changes in version 0.6.0 (2025-07-22)                  

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.