Package index • synergyMixR

All functions

aggregate_partial_results(): Aggregate Partial Results from PBS Jobs
align_clusters_to_truth(): Align Cluster Labels to Ground Truth Using Hungarian Algorithm
align_factor_signs_mfa(): Align (Flip) Factor Signs in an MFA Model
align_factor_signs_mpca(): Align (Flip) Factor Signs in a Mixture PCA Model
assign_by_kmeans(): K-means-based cluster assignment
bandpass_filter(): Bandpass Filter for EMG Signals
bootstrap_loading_ci(): Bootstrap Confidence Intervals for Synergy Loadings
calc_reconstruction_error_mixtureFA(): Compute SSE for a MixtureFA model
calc_reconstruction_error_mixturePCA(): Compute SSE for a MixturePCA model
calc_reconstruction_error_singleFA(): Compute Total Reconstruction SSE for Single FA
calc_reconstruction_error_singlePCA(): Compute Total Reconstruction SSE for Single PCA
calc_total_SST(): Compute the Total Sum of Squares (SST) for a List of Data Matrices
calibrate_model(): Compute Calibration from Model Fit
check_SSE_diagnostics(): Check SSE Diagnostics for Mixture Models
check_future_available(): Check if Future Packages are Available
check_pbs_job_status(): Check PBS Job Status
compare_baselines(): Compare Analysis Methods on v2 Dynamic Synergy Data
compare_static_vs_dynamic(): Compare Analysis Methods on Dynamic Synergy Data (v2)
compute_BIC(): Compute Bayesian Information Criterion (BIC)
compute_BIC_mfa(): Compute BIC for an MFA model
compute_BIC_mpca(): Compute BIC for a Mixture PCA Model
compute_ICL(): Compute ICL for a Mixture Model (Unified Interface)
compute_ICL_mfa(): Compute ICL for a Mixture Factor Analysis Model
compute_ICL_mpca(): Compute ICL for a Mixture PCA Model
compute_VAF(): Compute Global Variance Accounted For (VAF)
compute_VAF_mfa(): Compute VAF for a Fitted MFA Model
compute_VAF_mpca(): Compute VAF for a Mixture PCA (PPCA) Model
compute_ari(): Compute Adjusted Rand Index (ARI)
compute_ari_vaf_table(): Compile ARI and VAF into a Single Table
compute_bic_wrapper(): Compute BIC for a Fitted Model
compute_calibration(): Compute Calibration Statistics for Mixture Model
compute_cluster_sizes(): Compute Cluster Sizes from a Fitted MFA (or Mixture PCA) Model
compute_cluster_sizes_mpca(): Compute Cluster Sizes for Mixture PCA
compute_clusterwise_subspace_score(): Compute Cluster-wise Subspace Recovery Score
compute_ece(): Compute Expected Calibration Error
compute_entropy(): Compute Classification Entropy from Responsibilities
compute_factor_scores(): Compute Factor Scores for a Single MFA Cluster
compute_factor_scores_mfa(): Compute Factor Scores for All Subjects in an MFA Model
compute_factor_scores_mpca(): Compute PC Factor Scores for All Subjects in a Mixture PCA Model
compute_factor_scores_mpca_single(): Compute Factor (PC) Scores for a Single Subject in a Mixture PCA Cluster
compute_factor_scores_optimized(): Compute Factor Scores for a Single Subject in MFA
compute_global_vaf_mfa(): Compute Global VAF (1 - SSE/SST) for an MFA Model
compute_global_vaf_mpca(): Compute Global VAF (1 - SSE/SST) for Mixture PCA
compute_logLik(): Compute Log-Likelihood for Mixture Models
compute_logLik_mfa(): Compute the Mixture-Model Log-Likelihood for MFA
compute_logLik_mpca(): Compute the Mixture-Model Log-Likelihood for Mixture PCA
compute_mvc_reference(): Compute MVC Reference Values
compute_reconstruction_error(): Compute Reconstruction Error
compute_smoothness_penalty(): Compute Smoothness Penalty for Factor Scores
compute_subspace_similarity(): Compute Subspace Similarity Between Two Basis Matrices
compute_vaf_wrapper(): Compute VAF (Variance Accounted For) for a Fitted Model
compute_vss_criterion(): Compute VSS (Very Simple Structure) Criterion for Factor Loadings
count_completed_tasks(): Count Completed PBS Tasks
emg_preset_config(): EMG Preprocessing Preset Configurations
evaluate_model_selection(): Evaluate Model Selection Results
extract_cluster_labels(): Extract Cluster Labels from a Fitted Model
extract_envelope(): Extract Envelope from EMG Signal
extract_selection_summary(): Extract Selection Summary as Data Frame
extract_subject_features_by_singlePCA(): Extract subject-level features by single PCA
facet_mixsynergy(): Apply Consistent Faceting for Cluster and Factor Visualizations
fit_single_factor_analysis(): Fit a Single Factor Analysis (Ignoring Clusters)
fit_single_pca(): Fit a Single PCA (Ignoring Clusters)
fit_two_step_baseline(): Two-step Baseline Algorithm for PCA/FA
generate_pbs_script(): Generate PBS Job Script
generate_synergy_data(): Generate v2 Dynamic Synergy Data for Model Selection Experiments
get_model_by_K_r(): Retrieve a Specific Model by (K, r) from a Grid Search
get_model_by_K_r_mpca(): Retrieve a Mixture PCA Model by (K, r)
make_table1(): Create a Static Table of Simulation Conditions (Table 1)
make_table2(): Summarize Results for a Subset (Table 2)
mfa_em_fit(): Fit a Mixture Factor Analysis Model (with optional multi-initialization)
mfa_em_fit_cpp_singleInit(): Single-Run Mixture Factor Analysis with a Preset Initial Assignment
mixture_pca_em_fit(): Fit a Mixture PCA Model (Using mpcaTimeseriesCpp) with optional multi-initialization
mixture_pca_em_fit_cpp_singleInit(): Single-Run Mixture PCA with a Preset Initial Assignment
normalize_emg(): Normalize EMG Signal
notch_filter(): Notch Filter for Power Line Noise Removal
plot(<mixture_r_selection>): Plot Method for mixture_r_selection Objects
plot_activation_patterns(): Plot Activation Patterns
plot_aligned_basis_heatmaps(): Plot Aligned Basis Heatmaps
plot_all_factor_scores_mfa(): Plot All Factor Scores (Z) for MFA, Faceted by Cluster and Factor
plot_all_factor_scores_mpca(): Plot All Factor Scores (Z) for MPCA, Faceted by Cluster and Factor
plot_all_reconstructed_waveforms_mfa(): Plot Reconstructed Waveforms for All Clusters and Channels (MFA)
plot_all_reconstructed_waveforms_mpca(): Plot Reconstructed Waveforms for All Clusters and Channels (MPCA)
plot_basis_umap(): Plot Basis UMAP Embedding
plot_bic_comparison(): Plot BIC Comparison for Parameter Sweep
plot_bic_line(): Line Plot of BIC vs. K or r
plot_calibration(): Plot Calibration Curve
plot_cluster_embedding(): Plot Cluster Embedding
plot_cluster_synergy_loadings_mfa(): Plot Cluster-Specific Synergy Coefficients (Factor Loadings) for MFA
plot_cluster_synergy_loadings_mpca(): Plot Cluster-Specific Synergy Coefficients (Factor Loadings) for MPCA
plot_confusion_matrix(): Plot Confusion Matrix for Cluster Assignments
plot_fig1_ARI(): Plot ARI vs. Separation/Noise (Figure 1)
plot_fig1_baseline_comparison(): Plot Baseline Comparison (ARI and VAF)
plot_fig2_BIC_heatmap(): Plot BIC Heatmap for a Subset (Figure 2)
plot_fig3_init_boxplot(): Plot Boxplot of ARI for Multiple Initial Seeds (Figure 3)
plot_fig4to6_reconstruction_vaf(): Plot Reconstruction Error and VAF (Figures 4-6)
plot_metric_vs_axis(): Plot Metric vs Axis for Parameter Sweep
plot_neural_command(): Plot Neural Command Trajectories
plot_noise_profiles(): Plot Noise Profiles
plot_reconstructed_emg(): Plot Reconstructed EMG Signals
plot_sse_heatmap(): Plot SSE Heatmap Across Methods and Subjects
plot_sweep_summary(): Plot Summary Comparison for Parameter Sweep
plot_synergy_bases(): Plot Synergy Bases (Lambda Matrices)
plot_synergy_correlation_comparison(): Plot Synergy Correlation Comparison
plot_synergy_summary(): Plot Synergy Summary
plot_waveform_comparison_mfa(): Plot Original vs. Reconstructed Waveform for an MFA Model
plot_waveform_comparison_mpca(): Plot Original vs. Reconstructed Waveform for an MPCA Model
plot_winrate_heatmap(): Plot Win Rate Heatmap for Parameter Sweep
posthoc_mfa_evaluation(): Post-hoc Evaluation of MFA Grid Search
posthoc_mpca_evaluation(): Post-hoc Evaluation of Mixture PCA Grid Search
preprocess_emg(): EMG Preprocessing Pipeline
print(<bootstrap_ci>): Print Summary of Bootstrap CI Results
print(<calibration_result>): Print Summary of Calibration Results
print(<mixture_r_selection>): Print Method for mixture_r_selection Objects
print(<preprocessed_emg>): Print Method for preprocessed_emg Objects
read_all_results(): Read All Results from .rds Files
read_and_visualize_all(): Read All Results and Produce Tables/Figures
reconstruct_singleMPCA(): Reconstruct a single subject's data from a Mixture PCA model
rectify(): Rectify EMG Signal
remove_dc_offset(): Remove DC Offset from EMG Signal
rotate_mfa_model(): Post-hoc Rotation for an MFA Model
rotate_mpca_model(): Post-hoc Rotation for a Mixture PCA Model
run_k_selection_comparison(): Run K Selection Method Comparison (v3)
run_param_sweep(): Run Parameter Sweep Experiment
run_param_sweep_baselines(): Run Parameter Sweep for Baseline Method Comparisons (v2)
run_param_sweep_modelsel(): Run Parameter Sweep with Model Selection
run_single_task(): Run a Single Task from Parameter Grid
run_sweep_future(): Run Parameter Sweep Using Future
scale_color_mixsynergy(): Get Unified Color Scale for Discrete Categories
scale_fill_mixsynergy(): Get Unified Viridis Color Scale for Continuous Values
scale_fill_mixsynergy_discrete(): Get Unified Fill Scale for Discrete Categories
scale_fill_mixsynergy_diverging(): Get Unified Viridis Color Scale for Diverging Values
select_K_bootstrap_stability(): Select K Using Bootstrap Stability Selection
select_K_crossvalidation(): Select K Using Cross-Validation
select_K_r(): Unified Model Selection for K and r
select_optimal_K_r_mfa(): Search for the Best (K, r) for MFA via Grid Search (Using mfa_em_fit)
select_optimal_K_r_mfa_twostage(): Two-Stage Model Selection for MFA: Select K First, Then r
select_optimal_K_r_mpca(): Search for the Best (K, r) for Mixture PCA via Grid Search
select_optimal_K_r_mpca_twostage(): Two-Stage Model Selection for MPCA: Select K First, Then r
select_r_by_smoothness_mfa(): Select Optimal r by Smoothness Penalty for MFA
select_r_by_smoothness_mpca(): Select Optimal r by Smoothness Penalty for MPCA
select_r_by_vaf_mfa(): Select Minimum r Based on VAF Threshold for MFA
select_r_by_vaf_mpca(): Select Minimum r Based on VAF Threshold for MPCA
select_r_by_vss_mfa(): Select Optimal r by VSS Criterion for MFA
select_r_by_vss_mpca(): Select Optimal r by VSS Criterion for MPCA
select_r_clusterwise_mfa(): Select Optimal r Using Cluster-wise Evaluation for MFA
select_r_clusterwise_mpca(): Select Optimal r Using Cluster-wise Evaluation for MPCA
select_r_global_mfa(): Select Optimal r Using Global Model (FA or PPCA) with Cross-Validation
select_r_global_mpca(): Select Optimal r Using Global Model (PPCA) with Cross-Validation for MPCA
select_r_mixture(): Unified r Selection for Mixture Models (MFA/MPCA)
select_r_multicriteria_mfa(): Select Optimal r Using Multi-Criteria Approach for MFA
select_r_multicriteria_mpca(): Select Optimal r Using Multi-Criteria Approach for MPCA
setup_future_backend(): Setup Future Backend for HPC
show_parallel_options(): Print Summary of Available Parallelization Options
simulate_dynamic_synergy_data(): Simulate Dynamic Synergy Data with Three Orthogonal Control Axes
simulation_comparison: Simulation Comparison Functions
simulation_visualization: Simulation Visualization Functions
strip_attributes(): Strip All Attributes Except Essential Ones
submit_hpc_sweep(): Submit Parameter Sweep to HPC (Simple Interface)
submit_pbs_sweep(): Submit PBS Parameter Sweep
summarize_vaf_sse_mfa(): Summarize VAF and SSE for an MFA Model
summary(<mixture_r_selection>): Summary Method for mixture_r_selection Objects
summary(<preprocessed_emg>): Summary Method for preprocessed_emg Objects
theme_mixsynergy(): Unified Theme and Style Definitions for synergyMixR Visualizations
theme_mixsynergy_heatmap(): Get Unified Heatmap Theme for synergyMixR
theme_sweep(): Theme for Parameter Sweep Plots
visualize_twostep_diagnostics: Visualization Functions for Two-Step Algorithm Diagnostics
wait_for_pbs_job(): Wait for PBS Job Completion