A Python package for causal inference in quasi-experimental settings
-
Updated
Apr 30, 2025 - Python
A Python package for causal inference in quasi-experimental settings
A fast and easy to use, moddable, Python based Minecraft server!
Tools for the symbolic manipulation of PyMC models, Theano, and TensorFlow graphs.
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
A sphinx primer for ArviZ and PyMC contributors
An example plugin for the PyMine-Server plugin system
Distributed differentiable graph computation using PyTensor
Demonstrating the use of behavior-driven development (BDD) to Bayesian growth models for assumption tracking.
Replication materials for "The Relational Bases of Informal Financial Cooperation" (Simpson, In Prep.)
Bicyclus, a Bayesian Inference module for Cyclus
Code for paper "A Bayesian analysis of heart rate variability changes over acute episodes of bipolar disorder".
Python package of the 'Tumoroscope' model implemented in PyMC.
Testing deployment of PyMC models using MLFlow and BentoML.
Minecraft BE Edition Python Scripts
Core tools for use on client projects
Python version of McElreath's Statistical Rethinking package
SensibleSleep is an open-source Python package that implements a Hierarchical Bayesian model for learning sleep patterns from smartphone screen-on events.
My algorithms implementation to discover the main themes that pervade a large and otherwise unstructured collection of documents.
demonstration of uni-variate time series prediction by predicting monthly births in Sweden for the next 12 months
Add a description, image, and links to the pymc topic page so that developers can more easily learn about it.
To associate your repository with the pymc topic, visit your repo's landing page and select "manage topics."