amkatrutsa/QPFeatureSelection
Quadratic programming feature selection
GitHub repository with 7 stars and 4 forks.
Language: MATLAB
Topics: feature-selection, quadratic-programming, multicollinearity, test-data
Quadratic programming feature selection
GitHub repository with 7 stars and 4 forks.
Language: MATLAB
Topics: feature-selection, quadratic-programming, multicollinearity, test-data
2026-06-05: 7 stars and 4 forks.
Histology processing
GitHub repository with 77 stars and 28 forks.
Trending score: 0.18; stars gained: +0; forks gained: +0.
Language: MATLAB
Statistical NonParametric Mapping, development version
GitHub repository with 26 stars and 10 forks.
Trending score: 0.11; stars gained: +0; forks gained: +0.
Language: MATLAB
ClickHouse Matlab Driver
GitHub repository with 6 stars and 0 forks.
Trending score: 0.05; stars gained: +0; forks gained: +0.
Language: MATLAB
Topics: clickhouse, matlab, mex
Opticka is an experiment manager built on top of the Psychophysics toolbox (PTB) for MATLAB. It runs experimental tasks using flexible state machine logic and easily does dynamic methods-of-constants type experiments with full behavioural control. It uses a class system to create simple to use visual stimuli using experimenter friendly units. Opticka can use an Eyelink eyetracker, Plexon Omniplex neurophysiology data collection and general TTL control using either a cheap LabJack (ms precision) or DataPixx/Display++ (µs precision). It contains analysis routines linked to Fieldtrip for spike and LFP data easily parsed in terms of the experimental variables.
GitHub repository with 53 stars and 26 forks.
Trending score: 0.05; stars gained: +0; forks gained: +0.
Language: MATLAB
A Symbolic Package for Octave using SymPy
GitHub repository with 176 stars and 35 forks.
Trending score: 0.05; stars gained: +0; forks gained: +0.
Language: MATLAB
Topics: octave, symbolic, computer-algebra, mathematics
A MATLAB toolbox for exporting publication quality figures
GitHub repository with 1,344 stars and 366 forks.
Trending score: 0.04; stars gained: +0; forks gained: +0.
Language: MATLAB
A complete A-Z guide to Machine Learning and Data Science using Python. Includes implementation of ML algorithms, statistical methods, and feature selection techniques in Jupyter Notebooks. Follow Coursesteach for tutorials and updates.
GitHub repository with 58 stars and 27 forks.
Trending score: 0.05; stars gained: +0; forks gained: +0.
Language: Jupyter Notebook
Topics: classification, machine-learning, unsupervised-learning, python, scikit-learn, data-science