Monte Carlo Python Packages

dynesty

Dynamic Nested Sampling package for computing Bayesian posteriors and evidences

pyhepmc

Easy-to-use Python bindings for HepMC3

stochastic-rs

High-performance quantitative finance in Rust — 120+ stochastic processes, option pricing, calibration, fixed income, risk & copulas, with SIMD/GPU acceleration and Python bindings.

ebisu

Public-domain Python library for flashcard quiz scheduling using Bayesian statistics. (JavaScript, Java, Dart, and other ports available!)

cognitive-discovery-platform

Cognitive Discovery System — pure Python scientific computing. 18 modules: quantum, ML, NLP, graph theory, Monte Carlo, ODE solvers, FFT & signal filters, optimization, probability, statistics & time-series, symbolic math, numerical integration (1-D/2-D), knowledge graph. 1438 tests, 100% coverage, v1.2.3.

ultranest

Fit and compare complex models reliably and rapidly. Advanced nested sampling.

biosteam

The Biorefinery Simulation and Techno-Economic Analysis Modules

mujoco-mojo

A complete MJCF lifecycle and trial orchestration suite for MuJoCo, powered by Pydantic v2.

pmcx

Monte Carlo eXtreme (MCX) - Physically accurate and validated GPU ray-tracer

spindry

Low-cost host-guest conformer generation

pmcxcl

Monte Carlo eXtreme for OpenCL (MCXCL)

pmmc

Mesh-based Monte Carlo (MMC)

serpenttools

A suite of parsers designed to make interacting with SERPENT output files simple and flawless

modelx

Use Python like a spreadsheet!

owlplanner

Retirement planner with great wisdom

riskoptima

The RiskOptima toolkit is a comprehensive Python solution designed to assist investors in evaluating, managing, and optimizing the risk of their investment portfolios. This package implements advanced financial metrics and models to compute key risk indicators, including Value at Risk (VaR), Conditional Value at Risk (CVaR), and volatility assessme

sde-sim-rs

Powerful and flexible stochastic differential equation (quasi) Monte-Carlo simulation library written in Rust with Python bindings

cognitive-discovery-system

Cognitive Discovery System — pure Python scientific computing. 18 modules: quantum, ML, NLP, graph theory, Monte Carlo, ODE solvers, FFT & signal filters, optimization, probability, statistics & time-series, symbolic math, numerical integration (1-D/2-D), knowledge graph. 1438 tests, 100% coverage, v1.2.3.

snowline

Fit and compare complex models quickly. Laplace Approximation, Variational Bayes, Importance Sampling.

biorefineries

BioSTEAM's Premier Repository for Biorefinery Models and Results

promptmc

Infrastructure for safely using OpenMC with LLMs

risksim

Portfolio Monte Carlo risk simulation in Python for insurance risk, aggregate loss, capital modeling, and risk measures.

smol

Statistical Mechanics on Lattices

uqpy

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.