Package: rCISSVAE 1.0.1

Danielle Vaithilingam

rCISSVAE: Clustering-Informed Shared-Structure VAE for Imputation

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.

Authors:Yasin Khadem Charvadeh [aut], Kenneth Seier [aut], Katherine S. Panageas [aut], Danielle Vaithilingam [aut, cre], Mithat Gönen [aut], Yuan Chen [aut]

rCISSVAE_1.0.1.tar.gz
rCISSVAE_1.0.1.zip(r-4.7-any)rCISSVAE_1.0.1.zip(r-4.6-any)rCISSVAE_1.0.1.zip(r-4.5-any)
rCISSVAE_1.0.1.tgz(r-4.6-any)rCISSVAE_1.0.1.tgz(r-4.5-any)
rCISSVAE_1.0.1.tar.gz(r-4.7-any)rCISSVAE_1.0.1.tar.gz(r-4.6-any)
rCISSVAE_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
rCISSVAE/json (API)

# Install 'rCISSVAE' in R:
install.packages('rCISSVAE', repos = c('https://ciss-vae.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ciss-vae/rciss-vae/issues

Pkgdown/docs site:https://ciss-vae.github.io

Datasets:
  • clusters - Cluster assignments based on missingness patterns
  • 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

On CRAN:

Conda:

5.98 score 431 downloads 17 exports 88 dependencies

Last updated from:b13cacd854. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR226
source / vignettesOK291
linux-release-x86_64ERROR222
macos-release-arm64ERROR196
macos-oldrel-arm64ERROR186
windows-develERROR183
windows-releaseERROR209
windows-oldrelERROR188
wasm-releaseOK152

Exports:autotune_cissvaecheck_devicescluster_heatmapcluster_on_missingcluster_on_missing_propcluster_summarycreate_cissvae_envcreate_missingness_prop_matriximpute_with_cissvaeload_cissvae_modelload_impute_resultperformance_by_clusterplot_vae_architecturerun_cissvaesave_cissvae_modelsave_impute_resultupdate_cissvae_env

Dependencies:base64encbigDBiocGenericsbitopsbslibcachemcardscardxcirclizecliclueclustercodetoolscolorspacecommonmarkComplexHeatmapcpp11crayoncurldigestdoParalleldplyrevaluatefarverfastmapfontawesomeforeachfsgenericsGetoptLongGlobalOptionsgluegtgtsummaryherehighrhtmltoolshtmlwidgetsIRangesiteratorsjquerylibjsonlitejuicyjuiceknitrlabelinglatticelifecyclelitedownmagrittrmarkdownMatrixmatrixStatsmemoisemimepillarpkgconfigpngpurrrR6rappdirsRColorBrewerRcppRcppTOMLreactablereactRreticulaterjsonrlangrmarkdownrprojrootS4Vectorssassscalesshapestringistringrtibbletidyrtidyselecttinytexutf8V8vctrsviridisLitewithrxfunxml2yaml

Handling Binary and Categorical Variables
Rendered frombinary_variables_tutorial.rmdusingknitr::rmarkdown

Last update: 2026-04-08
Started: 2025-10-21

Saving and Using Saved Models
Saving a trained CISS-VAE model | Loading a saved model and imputing data
Rendered fromimpute_with_saved_model.rmdusingknitr::rmarkdown

Last update: 2026-04-08
Started: 2026-01-20

Summary Functions
Per-cluster Summary | Missingness Heatmap | By-cluster imputation loss function
Rendered fromsummary_functions.rmdusingknitr::rmarkdown

Last update: 2026-04-08
Started: 2025-10-13

Masking certain entries from imputation model
Understanding the 'Imputable' matrix
Rendered fromdo_not_impute_matrix.rmdusingknitr::rmarkdown

Last update: 2026-03-03
Started: 2025-09-18

rCISSVAE Vignette
Installing package | Ensuring correct virtual environment for reticulate | Virtual environment helper function | (optional) Installing other python packages | (optional) Use check_devices() to see available gpu devices | Quickstart | Run CISSVAE with Training History and Progress Tracking | Example Dataset | Explanation of run_cissvae() Parameters | Running the imputation | Clustering Features by Missingness Patterns | Using Pre-computed Missingness Proportion Matrix | Advanced Hyperparameter Optimization with Autotune | Understanding Parameter Types | Layer Placement Strategies
Rendered fromvignette.rmdusingknitr::rmarkdown

Last update: 2026-01-14
Started: 2025-08-21

Using Optuna Dashboard with rCISSVAE
What is Optuna Dashboard? | Creating an Optuna Dashboard with the Autotune Function
Rendered fromoptunadb.rmdusingknitr::rmarkdown

Last update: 2025-12-12
Started: 2025-11-18

Manual Virtual Environment Setup Tutorial
Prerequisites | Method 1: Creating Virtual Environment with R (reticulate) | Step 1: Install reticulate (if not already installed) | Step 2: Check available Python versions | Installing Python via reticulate (if needed) | Step 3: Create the virtual environment | Step 4: Activate the environment | Step 5: Install required packages | Step 6: Verify installation | Method 2: Creating Virtual Environment from Command Line | Step 1: Open terminal/command prompt | Step 2: Create virtual environment | Step 3: Activate the environment | Step 4: Upgrade pip and install packages | Step 5: Verify installation | Step 6: Connect R to your environment | Alternative: Using Conda Environment | Command Line Approach: | R Approach: | Troubleshooting Common Issues | Issue 1: "Python not found" | Issue 2: "Permission denied" errors | Issue 3: Package installation fails | Issue 4: CISS-VAE import fails in R | Issue 5: Environment not persisting between R sessions
Rendered fromvirtual_environment_tutorial.rmdusingknitr::rmarkdown

Last update: 2025-12-08
Started: 2025-08-21