mar

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

[ICCV2025]LazyMAR: Accelerating Masked Autoregressive Models via Feature Caching

A Noesis plugin to load textured models and animations from the master collection versions of MGS2 and MGS3

A One stop solution of Solutions for Ds and Algo and DSML for #scalerDSMLmar2022 batch students.

My personal simpler alternative to mdbook

This program is used to decompress and compress MAR archives used in Fossil Fighters game.

A library for synthetic missing data generation.

Welcome to visit my profile! ❤️❤️❤️❤️

Simple uncompressed random access archive format

How Different Types of Missingness affect a complete Dataset

Repositorio con enunciados, casos de prueba y soluciones de problemas de MAR2.

Healthcare missing-data analysis comparing complete-case, KNN, Bayesian iterative, and multiple imputation under MCAR, MAR, and MNAR.

Implementation of IEC 60601-2-44 Ed.4 Annex GG for objective evaluation of CT Metal Artifact Reduction (MAR) using DDOG CHO and AUC analysis.

1603:16:504 🇲🇦

Documents of the project

Bootcamp learning python on the zero-to-mastery platform

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.

A package for creating missing and faulty data

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

This is my website using GitHub Pages and markdown.