https://ciss-vae.r-universe.devRecent package updates in ciss-vaeR-universehttps://github.com/ciss-vae.pnghttps://ciss-vae.r-universe.devThu, 14 May 2026 13:12:39 GMTvaithid1@mskcc.org (Danielle Vaithilingam)Implements the Clustering-Informed Shared-Structure Variational Autoencoder ('CISS-VAE'), a deep learning framework for missing data imputation introduced in Khadem Charvadeh et al. (2025) <doi:10.1002/sim.70335>. The model accommodates all three types of missing data mechanisms: Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR). While it is particularly well-suited to MNAR scenarios, where missingness patterns carry informative signals, 'CISS-VAE' also functions effectively under MAR assumptions.https://github.com/r-universe/ciss-vae/actions/runs/29229635410Thu, 14 May 2026 13:12:39 GMTrCISSVAE1.0.1successhttps://ciss-vae.r-universe.devhttps://github.com/ciss-vae/rciss-vaebinary_variables_tutorial.rmdbinary_variables_tutorial.htmlHandling Binary and Categorical Variables2025-10-21 19:53:412026-04-08 13:25:43virtual_environment_tutorial.rmdvirtual_environment_tutorial.htmlManual Virtual Environment Setup Tutorial2025-08-21 17:23:292025-12-08 16:49:47do_not_impute_matrix.rmddo_not_impute_matrix.htmlMasking certain entries from imputation model2025-09-18 17:36:032026-03-03 14:20:11vignette.rmdvignette.htmlrCISSVAE Vignette2025-08-21 17:23:292026-01-14 19:19:44impute_with_saved_model.rmdimpute_with_saved_model.htmlSaving and Using Saved Models2026-01-20 14:38:532026-04-08 13:25:43summary_functions.rmdsummary_functions.htmlSummary Functions2025-10-13 17:02:592026-04-08 13:25:43optunadb.rmdoptunadb.htmlUsing Optuna Dashboard with rCISSVAE2025-11-18 14:12:342025-12-12 17:47:09