Machine Learning for Integrating Partially Overlapped Genetic Datasets


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Documentation for package ‘DataFusionGDM’ version 1.3.2

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apply_procrustes Procrustes alignment and mapping back to distances
besmi_batch_impute Run BESMI imputation for a list of dataset paths
besmi_create_masked_matrices Create masked matrices for BESMI
besmi_impute_single_dataset Impute a single dataset from masked matrix path
besmi_iterative_imputation Iterative imputation with MICE (tails-chain)
besmi_knn_impute KNN imputation sweep (uses VIM::kNN)
besmi_prepare_full_dataset Prepare full GDM dataset from CSV or RData
coords_to_distances Convert coordinate matrix to distance matrix
create_distance_heatmap Create a heatmap of genetic distances (ggplot2)
create_mds_plot Create MDS plot of genetic distances
export_simulated_gdm Export a simulated GDM to CSV
perform_mds Perform MDS on a pair of distance matrices
run_genetic_scenario Run simulation with predefined biological scenarios
run_genetic_simulation Run a high-level genetic simulation with configurable model
simulate_genetic_distances Simulate genetic distances using realistic population structure
visualize_results Create plotting handles for simulation results