NEWS

---

AutoMLR 1.0.0 (2026-06-07)

• CRAN pretest fix: replaced the extreme_surv_screen() regression test with a deterministic survival-only fixture using stepwise_cox, so the test no longer depends on optional glmnet behavior and remains stable on Windows and Debian R-devel incoming checks.
• Single-cohort report fix: cohort-aware survival, binary, continuous, and ordinal report tables now use a unified panel rule. A single real cohort is labelled by its cohort name, a literal All cohort is labelled Overall, and only multi-cohort analyses add Overall plus individual cohorts. This removes duplicated single-cohort panels such as Overall plus TCGA_LUAD_PanCan.
• Survival publication figures now adapt more layout settings to the actual rendered data: feature-importance and SHAP-style plots use data-driven left margins and point sizes, nomogram row spacing expands with the number of horizons, calibration and decision-curve legends choose less occupied plot corners, and the combination benchmark allocates relatively more space to the side performance bars.
• Binary publication figures now use the same data-aware plotting utilities for ROC, precision-recall, calibration, decision-curve, confusion-matrix, feature-importance, and benchmark plots, reducing label clipping and legend overlap in single- and multi-cohort outputs.
• Continuous and ordinal publication figures now compute ranking/importance margins from the longest model or feature label and reduce point size in observed-vs-predicted or observed-vs-score scatter plots as sample size increases. Ordinal confusion-matrix labels and cell counts also scale with the number and length of classes.
• DESCRIPTION wording was tightened for CRAN by removing unnecessary acronym parentheticals where the full method name is already written out.
• Routed model-evaluation and ensemble-fitting progress output through log_message() so initialize_auto_logging() captures messages such as Evaluating ... and Fitting ... in the log file as well as the console.
• Changed survival-SVM candidate evaluation to use k-fold resampling by default (surv_svm_resampling = "kfold") to avoid known single-row prediction failures from survivalsvm under leave-one-out cross-validation.
• Improved publication plotting defaults: heatmaps drop all-NA rows while marking partial NA cells, heatmap color scales adapt to the finite metric range, forest plots use data-aware x-axis limits, and continuous residual histograms use data-driven breaks and axis ranges.
• Added continuous and ordinal ensemble-member fitting progress messages.
• Exported automlr_input_to_surv_xy(), automlr_input_to_binary_xy(), automlr_input_to_continuous_xy(), and automlr_input_to_ordinal_xy() so users can call lower-level evaluation functions without relying on internal ::: helpers.
• Added optional automatic threshold recommendations for threshold-style ensemble selection: auto_min_cindex, auto_min_auc, auto_min_r2, and auto_min_qwk, controlled by auto_quantile.
• Added helper functions recommend_surv_cindex_threshold(), recommend_binary_auc_threshold(), recommend_continuous_r2_threshold(), and recommend_ordinal_qwk_threshold() for explicit threshold review.
• Moved heavyweight model engines from strong imports to optional suggested dependencies so installation no longer requires all modelling backends.
• Added check_automlr_dependencies() to report available and missing model backends, optional features, expected skip/degradation behavior, and install commands.
• Made logging and parallel execution degrade gracefully when optional log4r, future, or future.apply packages are unavailable.
• Added continuous-outcome workflows: prepare_continuous_cohort_input(), continuousmlr_parameters(), fit_continuous_ensemble(), export_continuous_results(), and render_continuous_report().
• Added ordinal-outcome workflows: prepare_ordinal_cohort_input(), ordinalmlr_parameters(), fit_ordinal_ensemble(), export_ordinal_results(), and render_ordinal_report().
• Added 18 default continuous/ordinal model variants across penalized regression, linear / stepwise linear regression, GBM, random forest, PCA-linear, and mean-baseline families.
• Continuous model selection defaults to out-of-fold RMSE, with MAE, R-squared, Pearson, Spearman, cohort performance, observed-vs-predicted, residual, and feature-importance diagnostics.
• Ordinal model selection defaults to out-of-fold quadratic weighted kappa, with accuracy, balanced accuracy, class MAE, score RMSE, Spearman, confusion-matrix, observed-score, and feature-importance diagnostics.
• Hardened the binary-classification workflow: multi-class outcomes are now rejected by default unless users explicitly request collapse_other = TRUE, and negative_class can be supplied for clear positive / negative mapping.
• Added binary preprocessing audits for missingness filtering, median imputation, zero / low-variance filtering, and optional feature standardization.
• Added binary k-fold and repeated k-fold resampling options while preserving LOOCV as the default.
• Split binary exported predictions into apparent and out-of-fold probabilities/classes; default diagnostic tables and plots now use out-of-fold probabilities.
• Fixed binary strategy = "threshold" report/export compatibility.
• Added binary model_performance_forest.csv and fig9_model_performance_forest with OOF ROC AUC and approximate 95% CI.
• Added a binary-classification workflow parallel to the survival workflow: prepare_binary_cohort_input(), binarymlr_parameters(), fit_binary_ensemble(), export_binary_results(), and binary summary helpers.
• Added 18 default binary model variants across 9 algorithm families: penalized logistic regression, standard / stepwise logistic regression, GBM, random forest, PCA-logistic, and Gaussian naive Bayes.
• Added binary single-model and single-/two-model probability-combination ranking by LOOCV ROC AUC, with PR-AUC, Brier score, threshold metrics, cohort AUC stability, calibration, DCA, confusion matrix, and feature importance exported as diagnostics.
• Added default explainability and clinical-utility outputs to regular survival exports: permutation feature importance, SHAP-style median-ablation summary and dependence plots, risk-score nomogram, calibration curve, decision curve analysis, and a model C-index forest plot.
• Refined the nomogram and SHAP-style figure set after FigureYa / regplot and SHAP documentation review: the nomogram now uses a points / total-points / event-risk ruler layout with risk-score distribution marks, while the SHAP figures follow mean-absolute-contribution bar, beeswarm summary, and dependence-with-density conventions.
• Added corresponding CSV audit tables: feature_importance.csv, shap_approx_contributions.csv, risk_score_nomogram.csv, calibration_curve.csv, dca_curve.csv, model_cindex_forest.csv, and risk_prediction_horizon.csv.
• Embedded the final publication figure set directly in the default HTML report while keeping diagnostic figures in a separate diagnostic folder.
• Clarified the four regular-analysis interpretation checkpoints in the default report bundle: data preparation, base-model screening, ensemble selection, and explainability / clinical utility.
• Added summarize_explainability_results() and bilingual interpretation text explaining that SHAP-style outputs are median-ablation approximations and that nomogram / calibration / DCA diagnostics are based on the final risk-score Cox calibration.
• Stored the training feature matrix inside fitted survival ensembles so exported explainability diagnostics can be regenerated from the fitted object.
• Validated the regular workflow on a 100-sample TCGA-LUAD test dataset with all publication figures, CSV tables, HTML report, and bilingual summaries generated successfully.

AutoMLR 0.1.0

• Added 18 default survival-model variants across 10 registry entries.
• Added LOOCV C-index evaluation, two-model all-subsets combination search, and weighted survival-risk ensembles.
• Added fold-level LOOCV parallelism and shared glmnet fits for lambda variants.
• Added cohort / resampling stability diagnostics while keeping C-index as the default selection criterion.
• Added direct fit_surv_ensemble(automlr_input) support so cohort labels from prepare_cohort_input() are used automatically for stability diagnostics.
• Added render_surv_report() to write an HTML report with separate figures/ and tables/ folders.
• Added export_surv_results() for batch output, including publication-ready figures, tables, fitted objects, and session metadata.
• Added training-set risk_scores.csv export and a risk-stratified Kaplan-Meier figure.
• Improved publication figures with Kaplan-Meier number-at-risk tables, cohort heatmap legends, and optional time-dependent AUC output.
• Added optional timeROC curve export and a complete all-single-model cohort C-index heatmap for full variant-level inspection.
• Added IRLS-inspired publication panels: a combination-by-cohort benchmark matrix, multi-cohort Kaplan-Meier panels, and multi-cohort timeROC panels.
• Deduplicated the default publication output to a final figure set: all-model heatmap, combination benchmark matrix, multi-cohort KM where estimable, and multi-cohort timeROC when timeROC is available.
• Added extreme_surv_screen() for two-stage extreme screening: apparent full-data upper-bound ranking followed by top-N 70/30 seed search.
• Added export_extreme_screen_results() with complete audit tables and a Morandi-toned extreme-screening figure set.
• Added summarize_extreme_screen_results() and automatic bilingual English / Chinese summary_report.md export to explain the best apparent models, seed-search leaders, train / validation C-index results, cohort diagnostics, and failure notes.
• Added regular-analysis Markdown summary templates for data preparation, base-model screening, and ensemble selection. render_surv_report() and export_surv_results() now write bilingual English / Chinese summaries by default.
• Changed the regular ensemble default to search single- and two-model candidates (min_models = 1, max_models = 2) so users can directly compare the best single model with the best two-model combination.
• Added a figure-rich tutorial at inst/tutorials/AutoMLR_tutorial.md, with standalone code blocks and interpretation guides for regular analysis and extreme screening.