Changes in version 0.2.0                        

New functions

  - generate_aft_dgm_flex() — general data-generating model (DGM)
    builder. Accepts any survival dataset and fits an accelerated
    failure time (AFT) super-population model with user-specified
    treatment effect heterogeneity parameters. This is the recommended
    starting point when building a DGM based on a dataset other than
    GBSG.

  - simulate_from_dgm() — general simulator for drawing trial replicates
    from an aft_dgm_flex DGM. Supersedes simulate_from_gbsg_dgm() for
    new code. Column names in the returned data frame use underscore
    notation (y_sim, event_sim, treat_sim, flag_harm).

  - run_simulation_analysis() (general version) — simulation wrapper
    that calls simulate_from_dgm() and accepts explicit column-name
    parameters, making it applicable to any DGM built with
    generate_aft_dgm_flex() or setup_gbsg_dgm(). The GBSG dataset is one
    application of this general pipeline rather than a separate code
    path.

  - setup_gbsg_dgm() — the recommended entry point for all GBSG-based
    simulation work. Encodes the data preparation and subgroup
    definition from León et al. (2024) and returns an
    aft_dgm_flex-compatible object accepted by simulate_from_dgm() and
    run_simulation_analysis(). Existing scripts using create_gbsg_dgm()
    can migrate with a one-line change: dgm <- setup_gbsg_dgm(model =
    "alt", k_inter = k, seed = seed).

Superseded functions

create_gbsg_dgm() is superseded by setup_gbsg_dgm(). It remains fully
functional and continues to produce correct results; no existing GBSG
simulation scripts need to change. The distinction is that
setup_gbsg_dgm() returns an object of class c("aft_dgm_flex",
"gbsg_dgm") compatible with the general pipeline, whereas
create_gbsg_dgm() returns only "gbsg_dgm". A .Deprecated() signal is
emitted to encourage migration in new code.

simulate_from_gbsg_dgm() is superseded by simulate_from_dgm() for new
code. Column names in the output change from dot-notation to underscore
notation — see the mapping table below. Pass analysis_time = Inf to
match the legacy max_follow = Inf behaviour.

| Legacy column | General column | |---------------|----------------| |
y.sim | y_sim | | event.sim | event_sim | | treat | treat_sim | |
flag.harm | flag_harm |

Superseded parameters

  - run_simulation_analysis(max_follow) → use analysis_time. If
    supplied, max_follow is forwarded to analysis_time with a warning.

  - run_simulation_analysis(muC_adj) → use cens_adjust. If supplied,
    muC_adj is forwarded to cens_adjust with a warning.

Bug fixes

The following bugs were discovered and fixed during the general pipeline
migration. All affected code paths were exercised by GBSG factor
variables (v1–v7) stored as factor() rather than numeric().

  - lasso_selection() (get_FSdata_helpers.R): as.matrix() on a data
    frame containing factor columns produced a character matrix that
    cv.glmnet() rejected. Factor columns with all-numeric levels are now
    coerced via as.integer(as.character(.)) before matrix conversion.

  - process_conf_force_expr() (get_FSdata_helpers.R): mean() applied to
    a factor column returned NA. Factor columns are now coerced to
    numeric before mean(), median(), and quantile() calls.

  - evaluate_comparison() (forestsearch_helpers.R): the <= / >= operator
    applied to a factor column triggered an Ops.factor warning and
    returned NA. Factor columns are now coerced to numeric before
    comparison.

  - forestsearch() (forestsearch_main.R): df[, conf.screen] dropped to a
    vector when conf.screen had length 1, causing dummy() to error on a
    non-data-frame input. Fixed by adding drop = FALSE.

  - default_grf_params_gen() (run_simulation_analysis.R): maxdepth was
    initialised to 4, exceeding the maximum of 3 accepted by
    grf.subg.harm.survival(). Corrected to 2 (matching the legacy
    default).

  - default_grf_params_gen() (run_simulation_analysis.R): sg.criterion
    was set to "hr", which is not a valid value. Corrected to "mDiff"
    (matching the legacy default).

Internal changes

  - create_gbsg_dgm() and simulate_from_gbsg_dgm() are now thin public
    wrappers that call .create_gbsg_dgm_() and
    .simulate_from_gbsg_dgm_() internally. This prevents warning spam in
    functions that call these in loops or binary searches
    (calibrate_k_inter(), get_dgm_with_output(),
    validate_k_inter_effect()).

  - compute_dgm_cde() now resolves the super-population data frame from
    dgm$df_super_rand (GBSG DGMs) or dgm$df_super (general aft_dgm_flex
    DGMs), making it compatible with both class hierarchies.

  - globals.R: added "sim_id" to utils::globalVariables() to suppress a
    spurious R CMD check NOTE from run_simulation_analysis.R.

                 Changes in version 0.1.0 (2026-03-23)                  

  - Initial release.