AntsNet-package         AntsNet: Unified Simulation of Ant Colony /
                        Machine Learning Isomorphisms
acar                    Ant Colony Adaptive Recruitment (ACAR)
adaboost                AdaBoost with Decision Stumps
calculate_margins       Calculate Boosting Margins
calculate_quorum_margin
                        Calculate Quorum Margin
colony_variance_experiment
                        Experiment 2: Ant Colony Variance Decomposition
convergence_experiment_boost
                        Convergence Rate Experiment
create_isomorphism_schematic
                        Figure 1: Isomorphism Schematic
find_best_stump         Find the Best Decision Stump
gacl                    Generational Ant Colony Learning (GACL)
generate_all_figures    Generate All Manuscript Figures
generate_classification_data
                        Generate Synthetic Classification Data
generate_regression_data
                        Generate Synthetic Regression Data
generate_synthetic_data
                        Generate Synthetic Classification Data
isomorphism_test        Experiment 3: Direct Isomorphism Test
launch_app              Launch an Interactive Shiny App
noise_experiment_boost
                        Noise Robustness Experiment
optimal_decorrelation_experiment
                        Experiment 4: Optimal Decorrelation
plot_colony_accuracy    Supplementary: Colony Accuracy vs Size
plot_convergence_boost
                        Plot Figure 4: Convergence Rates
plot_convergence_complexity
                        Plot Convergence Across Complexity (Figure 6)
plot_correlation_decay
                        Figure 3: Correlation Decay Comparison
plot_gradient_dynamics
                        Plot Gradient Dynamics (Figure 7)
plot_isomorphism        Plot the Gradient Descent Isomorphism (Figure
                        1)
plot_learning_curves    Plot Learning Curves with Replicates (Figure 2)
plot_learning_rate_sensitivity
                        Plot Learning Rate Sensitivity (Figure 4)
plot_margin_quorum      Plot Figure 3: Margin vs Quorum
plot_noise_robustness_boost
                        Plot Figure 5: Noise Robustness
plot_noise_robustness_nn
                        Plot Noise Robustness (Figure 5)
plot_optimal_decorrelation
                        Figure 4: Optimal Decorrelation
plot_pheromone_weight   Plot Pheromone vs Weight Evolution (Figure 3)
plot_plasticity         Plot Plasticity and Adaptation (Figure 8)
plot_sensitivity_heatmap
                        Figure 5: Sensitivity Heat-map
plot_variance_decomposition
                        Figure 2: Variance Decomposition
plot_weak_learnability
                        Plot Figure 1: Weak Learnability Theorem
plot_weight_pheromone   Plot Figure 2: Weight vs Pheromone Evolution
predict_adaboost        Predict with an AdaBoost Ensemble
predict_stump           Predict with a Decision Stump
sensitivity_analysis    Experiment 5: Sensitivity Analysis
sim_boost_recruitment   Boosting and Adaptive Recruitment Simulation
sim_colony_convergence
                        Colony Convergence Simulation
sim_decorrelation       Decorrelation Parameter Sweep
sim_gradient_colony     Gradient Descent and Generational Colony
                        Learning
sim_margin_analysis     Margin Analysis
sim_plasticity          Plasticity and Environmental Adaptation
sim_variance_decomp     Variance Decomposition Simulation
simple_neural_network   Simple Neural Network with Stochastic Gradient
                        Descent
simulate_ant_colony     Simulate an Ant Colony Decision Process
test_isomorphism        Test Isomorphism Between Two Learning Curves
track_weights           Track AdaBoost Weight Evolution
variance_decomposition_experiment
                        Experiment 1: Random Forest Variance
                        Decomposition
weak_learnability_experiment
                        Weak Learnability Experiment
within_colony_correlation
                        Compute Within-Colony Ant Correlation
