Stars
This is the repository for the code that accompanies the second edition of "A Swift Kickstart".
BridgeStan provides efficient in-memory access through Python, Julia, and R to the methods of a Stan model.
Causal inference, graphical models and structure learning in Julia
Storage for results of Bayesian inference
Port of Statistical Rethinking (2nd edition) code to Julia
Quickly and easily install a GitHub Action on all repositories in a GitHub organization
Bayesian inference with probabilistic programming.
Comparing performance and results of mcmc options using Julia
Generate Julia package skeletons using a simple template system
Read samples from CmdStan into vectors of the appropriate Julia type.
Julia library for dumping data to be read by Stan.
Examples for Bayesian inference using DynamicHMC.jl and related packages.
Transformations to contrained variables from ℝⁿ.
Markov Chain Monte Carlo convergence diagnostics in Julia
A common framework for implementing and using log densities for inference.
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
Probabilistic programming via source rewriting
Documentation and tutorials for the Turing language
Types and utility functions for summarizing Markov chain Monte Carlo simulations
Plots in Julia using the PGFPlots LaTeX package
A meta package for data science in julia