LifetechOCMS - A Secure and Modular PHP Framework

Cognitive Models for Subjective Scales and Decision Making Tasks in R

Hierarchical matrices tool box

Python interface for htool

This project implements an AutoML-driven adaptive drift detection framework for COVID-19 forecasting, leveraging ADWIN & DDM to dynamically adjust models in response to concept drift. It enhances predictive accuracy by intelligently retraining models, ensuring robust time-series forecasting under evolving data conditions.

Manage Windows brightness

Final project for the course "Introduction to Neuroscience" (2024 Fall).

R package that provides estimation methods for Gravity Models

This .Net/.Net Core class library is used to interface with existing IBM i database, program calls, CL commands, service programs and data queues via the PASE based xmlservice-cli PASE command program or regular qsh/bash commands. qsh/bash commands can be used to interface with any qsh/pase based utilities such as the IBM i db2util utility

Boundary Element Method in 2D based on Crouch & Starfield book

Successor of Poly3D and iBem3D

Analysis of behavioral data (reaction time and accuracy) in the RDM task, including investigation of the learning effect and fitting RDM and Wang models to the data.

Cognitive Models: Computational Modeling of Cognitive Processes with Bayesian Mixed Models in Julia

Documentation, Instructions and other helpful information regarding working with the "Data Donation Module" (DDM-UZH)

Neural drift-diffusion model (NDDM) is a repository to integrate simultaneously both single-trial EEG measures and behavioral performance (response time and accuracy) to understand cognition.

Website with data and code for our manuscript on respiratory brain coupling in perception.

Project to convert from various coordinate types to other coordinate types.

The Tornado 🌪️ framework, designed and implemented for adaptive online learning and data stream mining in Python.

Python GUI for the quick processing, analysis and plotting of differential dynamic microscopy data

PyBEAM: A Bayesian approach to parameter inference for a wide class of binary evidence accumulation models