Clustering-Informed Shared-Structure VAE for Imputation

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Documentation for package ‘rCISSVAE’ version 0.0.4

• 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
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