🎨 Visualisation toolbox for beautiful and publication-ready figures

A suite of psychometric tests used in MeRID project

Python package for calculating various information measures, including entropy, mutual information, transfer entropy, and more, with support for both discrete and continuous variables.

Statistical Rethinking Course 2026

Code and Data for An incremental information-theoretic buffer supports sentence processing.

R package for interfacing with Julia's GLMM library

The book "Embrace Uncertainty: Fitting Mixed-Effects Models with Julia"

Intro Bayes Course for SMLP 9+

SMLP2025: Advanced methods in frequentist statistics with Julia

Train transformer language models with reinforcement learning.

Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization [ACL 2026]

SAP Benchmark

Code and statistical results for the paper: The linearity of the effect of surprisal on reading times across languages

Tools for checking ACL paper submissions

Qwen3-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.

WIP: Wrapper package for the sample method in Stan's cmdstan executable.

Access a database of word frequencies, in various natural languages.

LM training using GPT-NeoX ("Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens," Findings of EMNLP23)

Simple tool for generating tokens with open source transformers and/or calculate per-token surprisal.

Model zoo for different kinds of uncertainty quantification methods used in Natural Language Processing, implemented in PyTorch.

OneStop: A 360-Participant Eye Tracking Dataset with Different Reading Regimes

The repo for paper "Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data"

Simulation tools for Mixed Models

A unified interface for computing surprisal (log probabilities) from language models! Supports neural, symbolic, and black-box API models.

In this repository we keep the code for the implementation of the eye-tracking experiment for the COST action MultiplEYE. The eye-tracking-while-reading experiment is implemented using Python.

Python and torch-based package implementing various parametric and nonparametric methods for conditional density estimation

A python package for processing eye movement data