autotune_cissvae        Autotune CISS-VAE hyperparameters with Optuna
check_devices           Check PyTorch device availability
cluster_heatmap         Cluster-wise Heatmap of Missing Data Patterns
cluster_on_missing      Cluster on Missingness Patterns
cluster_on_missing_prop
                        Cluster Samples Based on Missingness
                        Proportions
cluster_summary         Cluster-wise summary table using a separate
                        cluster vector (gtsummary + gt)
clusters                Cluster assignments based on missingness
                        patterns
create_cissvae_env      Create or reuse a CISSVAE Python virtual
                        environment
create_missingness_prop_matrix
                        Create Missingness Proportion Matrix
df_missing              Sample dataset with missing values
dni                     Example dni matrix for demo of imputable_matrix
impute_with_cissvae     Impute new data with a loaded Python CISS-VAE
                        model
load_cissvae_model      Load a saved Python CISS-VAE model
mock_surv               Example survival data for demo of
                        imputable_matrix
performance_by_cluster
                        Compute per-cluster and per-group performance
                        metrics (MSE, BCE)
plot_vae_architecture   Plot VAE Architecture Diagram
run_cissvae             Run the CISS-VAE pipeline for missing data
                        imputation
save_cissvae_model      Save a trained Python CISS-VAE model to disk
