{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CISS-VAE Quickstart\n", "\n", "The **Clustering-Informed Shared-Structure Variational Autoencoder (CISS-VAE)** is a flexible deep learning model for missing data imputation that 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. Please see our publication for more details.\n", "\n", "\n", "\n", "## Installation\n", "The CISS-VAE package is currently available for python, with an R\n", "package to be released soon. It can be installed from either\n", "[github](https://github.com/CISS-VAE/CISS-VAE-python) or PyPI.\n", "\n", "``` bash\n", "# From PyPI \n", "pip install ciss-vae\n", "\n", "```\n", "\n", "``` bash\n", "# From GitHub (latest development version)\n", "pip install git+https://github.com/CISS-VAE/CISS-VAE-python.git\n", "```\n", "\n", "