Package index • MOBID

All functions
`BiomarkerPanelResult-class` `BiomarkerPanelResult`	Biomarker Panel Result Class
`OptimizationResult-class`	Optimization Result Class
`TransferablePanelResult-class`	Transferable Biomarker Panel Result Class
`analyze_feature_stability()`	Analyze Feature Selection Stability Across Pareto Solutions
`batch_associated_features`	Batch-Associated Feature Selection
`build_objectives()`	Build objective descriptors from registered metrics.
`calibrate_panel()`	Calibrate a Panel with Neyman-Pearson Threshold
`compute_diversity()`	Annotation-free co-expression diversity for Pareto solutions
`compute_inclusion_frequencies()`	Compute biomarker inclusion frequencies across optimisation runs.
`construct_dual_stream_ratios()`	Construct Dual-Stream Ratio Features
`de_features`	Differential Expression Feature Selection
`define_objectives()`	Define optimization objectives.
`discriminative_features`	Discriminative Feature Selection with Cohort Penalty
`dual_stream_ratios`	Dual-Stream Ratio Feature Construction
`ensure_binary_response()`	Validate Binary Response
`evaluate_cohorts`	Per-Cohort Panel Evaluation
`evaluate_panel()`	Core Panel Evaluation
`evaluate_panel_at_sensitivity()`	Evaluate Panel at Target Sensitivity
`evaluate_panel_by_cohort()`	Evaluate a Biomarker Panel by Cohort
`evaluate_pareto_solutions()`	Evaluate Pareto Solutions on Held-Out Data
`evaluate_sensitivity`	Sensitivity-Based Panel Evaluation
`feature_stability`	Feature Stability Selection
`feature_stability_analysis`	Feature Selection Stability Analysis
`feature_transform_registry()`	List registered feature transform functions
`find_threshold_for_sensitivity()`	Find Threshold for Target Sensitivity
`fit_np_panel()`	Fit a Neyman-Pearson Panel from Optimization Results
`fit_panel()`	Fit a Panel Model from Optimization Results
`fitness_cv`	CV / In-Sample NSGA Fitness
`fitness_dispatch`	Fitness Strategy Dispatch
`fitness_loco`	Leave-One-Cohort-Out NSGA Fitness
`fitness_validation`	Validation-Based NSGA Fitness
`get_solution_base_features()`	Get Solution Base Features
`get_solution_features()`	Get Solution Features
`hypervolume()`	Hypervolume of an Optimization Result
`hypervolume_history()`	Hypervolume Trajectory Across NSGA Generations
`metric_classification`	Classification Metric Functions
`metric_cohort`	Cohort-Aware Metric Functions
`metric_discrimination`	Discrimination Metric Functions
`metric_registry()`	Metric Function Registry
`metric_specificity_at_sensitivity()`	Specificity at a Fixed Sensitivity Threshold
`min_metric_constraint()`	Minimum metric constraint constructor.
`n_solutions()`	Number of Solutions
`nonredundant_features`	Redundancy-Aware Feature Selection
`np_fit`	Neyman-Pearson Panel Fitting
`np_threshold()`	NP Threshold Accessor
`nsga_history()`	NSGA Generation History
`optimize_panel()`	Optimize Biomarker Panels with NSGA-III / NSGA-II
`optimize_panel_transferable()`	Optimize Biomarker Panels with Transferability Focus (Deprecated)
`pairwise_col_diff()`	Compute pairwise difference between matrix columns
`panel_base_features()`	Panel Base Features Accessor
`panel_features()`	Panel Features Accessor
`panel_metrics()`	Panel Metrics Accessor
`panel_model()`	Panel Model Accessor
`per_cohort_metrics()`	Per-Cohort Metrics Accessor
`plot_cohort_comparison()`	Plot Per-Cohort Metric Comparison
`plot_feature_stability()`	Plot Feature Inclusion Frequencies
`plot_pareto_evolution()`	Animate Pareto Front Evolution Alongside Feature Inclusion
`plot_pareto_front()`	Plot Pareto Front on Held-Out Data
`register_feature_transform()`	Register a feature transform function
`register_metric()`	Register a metric function.
`ruleout_features`	Rule-Out Feature Selection
`ruv_features`	RUV-4 Dual-Objective Feature Selection
`ruv_features_helpers`	RUV Feature Selection Helpers
`select_batch_associated_features()`	Select Batch-Associated Features
`select_de_features()`	Select Top Differentially Expressed Features
`select_discriminative_features()`	Select Discriminative Features with Cohort Penalty
`select_features_for_ratios()`	Select Features for Ratio Construction
`select_nonredundant_features()`	Select Non-Redundant Features by Greedy mRMR
`select_panel_inclusion_frequency()`	Assemble a panel by inclusion frequency.
`select_ruleout_features()`	Select Rule-Out Features by Specificity at a Sensitivity Target
`select_ruv_features()`	Select Features via RUV-4 Dual-Objective Scoring
`select_transferable_features()`	Select Ridge-Stable Transferable Features
`simulate_expression_data()`	Simulate gene expression data for biomarker panel testing
`solutions()`	Solutions Accessor
`summarize_solutions()`	Pareto solution utilities and selection strategies
`transferable_features`	Transferable Feature Selection
`transform-registry`	Feature Transform Registry
`weighted_variance()`	Weighted Variance Accessor

Reference

All functions