PyGrinder: a Python toolkit for grinding data beans into the incomplete for real-world data simulation by introducing missing values with different missingness patterns, including MCAR (complete at random), MAR (at random), MNAR (not at random), sub sequence missing, and block missing

• Updated May 5, 2026
• Python

Time-series missingness simulation separating mechanisms (MCAR/MAR/MNAR) from patterns (pointwise/block/monotone/decay/markov). Weighted multi-driver MAR, exact rate control, automatic calibration, 2D/3D support. Built for reproducible imputation benchmarking.

• Updated May 17, 2026
• Python

A library for synthetic missing data generation.

• Updated Jun 4, 2023
• MATLAB

How Different Types of Missingness affect a complete Dataset

• Updated Nov 21, 2022
• Jupyter Notebook

• Updated Aug 13, 2020
• Jupyter Notebook

Este estudio investiga la efectividad de la imputación múltiple en el análisis factorial confirmatorio (AFC) con datos de liderazgo, donde se simularon valores perdidos (MCAR) en un 40% de la muestra.

• Updated Sep 20, 2024
• R

A package for creating missing and faulty data

• Updated May 6, 2019
• R

Reproducible data science pipeline to evaluate whether physicochemical water parameters can predict potability, including missingness mechanism assessment, cleaning, SQL exploration, and non-linear modeling.

• Updated Feb 20, 2026
• Jupyter Notebook

A comparative study of missing data imputation methods across MCAR, MAR, and MNAR mechanisms using classical, probabilistic, and deep learning approaches.

• Updated May 7, 2026
• Jupyter Notebook

FAI (Feature-Wise Adaptive Imputation) is a machine learning framework that automatically selects the best imputation method per feature based on statistical properties — optimizing for downstream predictive performance, not just imputation error.

• Updated Jun 16, 2026
• Jupyter Notebook