# Clustering-Informed Shared-Structure VAE for Imputation

## Documentation for package ‘rCISSVAE’ version 0.0.5

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

autotune_cissvae | Autotune CISS-VAE hyperparameters with Optuna
check_devices | Check PyTorch device availability
clusters | Cluster assignments based on missingness patterns
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)
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