BridgeStan provides efficient in-memory access through Python, Julia, and R to the methods of a Stan model.
BSD-3-CLAUSE License
Exposing Stan functions in Python
convenient python functions for bayesian GLMs (using Stan / PyStan)
Differentiable Factor Graph Optimization for Learning Smoothers @ IROS 2021
Easy, minimal interface to the Stan samplers in several languages
Collection of reinforcement learning algorithms
High-Performance Symbolic Regression in Python and Julia
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many hum...
A sklearn style interface to Stan regression models
Stan keywords and functions; used to create editor language modes and syntax highlighters
An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DP...