Pymc Python Packages

causalpy

A Python package for causal inference in quasi-experimental settings

ptgp

GPs in PyTensor

oreum-core

Core tools for use on client projects

calibr8

Toolbox for non-linear calibration modeling.

bayesian-pricing

Hierarchical Bayesian models for insurance pricing — PyMC 5, thin-data segments, credibility factors

insurance-spatial

BYM2 spatial territory ratemaking and conformal prediction intervals for UK personal lines insurance

survivalpredict

python survival analysis/survival curves, with a scikit-learn-like api.

actuarcredibility

Credibility models for actuarial pricing: Bühlmann, Bühlmann-Straub, Jewell, Hachemeister, and Bayesian extensions.

levy-stable-jax

Lévy's alpha-stable distribution for the Jax numerical framework

simba-mcp

Model Context Protocol (MCP) server for SIMBA — connect Claude, Cursor, and AI assistants to Bayesian Marketing Mix Models (MMM)

pytensor-federated

Distributed, differentiable and async graph computation for PyTensor

bioanchor

🔬 Autonomous Bayesian scientific agent — MCMC analysis, uncertainty quantification, and permanent decentralized archiving. The reproducibility layer for DeSci.

tumoroscope-pymc

Python package of the 'Tumoroscope' model implemented in PyMC.