A Python toolbox/library for reality-centric machine/deep learning and data mining on partially-observed time series with PyTorch, including SOTA neural network models for science analysis tasks of imputation, classification, clustering, forecasting & anomaly detection on incomplete (irregularly-sampled) multivariate TS with NaN missing values

Imputation of missing values in tables.

Multivariate Imputation by Chained Equations

Data imputations library to preprocess datasets with missing data

A unified multi-task time series model.

The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516

HandySpark - bringing pandas-like capabilities to Spark dataframes

Codebook from my GWAS cookbook

(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

CRAN R Package: Time Series Missing Value Imputation

A framework for prototyping and benchmarking imputation methods

Official repository for the paper "Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks" (ICLR 2022)

Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).

Time Series Data Beans: a Python toolbox loads 169 public time-series datasets for machine learning/deep learning with a single line of code.

MOMENT: A Family of Open Time-series Foundation Models

Beta Machine Learning Toolkit

Race and ethnicity Imputation from Disease history with Deep LEarning

Making imputation easy

Accurate and robust imputation of scRNA-seq data

Awesome Deep Learning Resources for Time-Series Imputation, including a must-read paper list about using deep learning neural networks to impute incomplete time series containing NaN missing values/data