immunaut 1.0.3

• Machine Learning Data Leakage Fix: Enforced strict train/test data isolation in auto_simon_ml(). The dataset is partitioned into training and testing sets prior to any preprocessing transformations.
• Preprocessing Isolation: preProcessData() and preProcessResample() estimate imputation, centering, scaling, and zero-variance filtering parameters exclusively on the training partition and apply them to the test set without information leakage.
• Fitted Preprocessing Object: Added preProcessParams to the auto_simon_ml() output object for reproducible transformations on external validation datasets.
• Multi-Class Evaluation: Added MLmetrics to Imports and fixed multi-class Macro-F1 score calculation to correctly extract class-level metrics from confusionMatrix$byClass.
• Vectorized Metric Normalization in pick_best_cluster_overall(): Resolved a scalar normalization bug where candidate cluster scores were collapsed to 0.5. Candidate metrics (modularity, silhouette, Davies-Bouldin index, Calinski-Harabasz index) are now normalized across all candidate partitions simultaneously, with index alignment preserved.
• Robust NA Handling in normalize(): Handled NAs gracefully via range(..., na.rm = TRUE) to prevent missing value where TRUE/FALSE needed errors on incomplete vectors.
• Safe Comparison in pick_best_cluster_modularity() & pick_best_cluster_silhouette(): Added !is.na() checks to ensure clustering selection does not crash when candidates contain missing metric values.
• Clean Default Handling in immunaut(): Automatically generates fileHeader if NULL and defaults selectedColumns safely to avoid dropping all features.
• Preserved Rows in immunaut(..., removeNA = TRUE): Properly aligned dataset_ml rows with filtered observations so that clustering labels are retained even after na.omit() drops incomplete cases.
• Adaptive t-SNE and Louvain Parameter Clamping:
  • calculate_tsne() respects user-specified seeds (falling back to 1337) and dynamically clamps perplexity to the maximum mathematical threshold allowed by sample size ((n - 1) / 3).
  • Safe zero-variance feature filtering using isTRUE(stats::var(., na.rm = TRUE) > 0) prevents tidyselect predicate evaluation crashes on all-NA or constant columns.
  • cluster_tsne_knn_louvain() dynamically adjusts knn_clusters whenever \(k \ge n\) to prevent FNN::get.knn crashes and graph construction failures.
• DBSCAN and Outlier Factor Alignment: DBSCAN noise points (labeled 0 or NA) in cluster_tsne_density(), cluster_tsne_mclust(), and cluster_tsne_hierarchical() are properly mapped to cluster “100” with factor levels explicitly updated, eliminating accidental NA conversions.
• Plotting Improvements in plot_clustered_tsne():
  • Eliminated global R session modification caused by theme_set(), applying themes locally to the ggplot object.
  • Preserves non-numeric/string cluster identifiers as valid factors instead of coercing them to NA via as.numeric().
  • Removed stray debug print() statements in is_var_empty().
• Comprehensive Unit Testing: Added a test suite (test-auto_simon_ml.R and test-pipeline_and_fixes.R) with 43 assertions covering ML isolation, clustering methods, edge cases, NA robustness, and visualization.

immunaut 1.0.2

• Add datasets: Added new datasets - https://zenodo.org/records/14719593
• Add examples: Added examples for new datasets