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.

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

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

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

This is my website using GitHub Pages and markdown.

There are multiple file in this every file having different way of doing drowsiness detection . You can perform task with any one of them and don`t forget to upload the sound folder and the 68 face detection dat file in this which will help to detect the landmark on your face it will detect by seeing your EAR and MAR.