ArviZ.jl: Exploratory analysis of Bayesian models in Julia

ArviZ.jl is a Julia meta-package for exploratory analysis of Bayesian models. It is part of the ArviZ project, which also includes a related Python package.

ArviZ consists of and re-exports the following subpackages, along with extensions integrating them with InferenceObjects:

• InferenceObjects.jl: a base package implementing the InferenceData type with utilities for building, saving, and working with it
• MCMCDiagnosticTools.jl: diagnostics for Markov Chain Monte Carlo methods
• PSIS.jl: Pareto-smoothed importance sampling
• PosteriorStats.jl: common statistical analyses for the Bayesian workflow

Additional functionality can be loaded with the following packages:

• ArviZExampleData.jl: example InferenceData objects, useful for demonstration and testing
• ArviZPythonPlots.jl: Python ArviZ's library of plotting functions for Julia types

See the navigation bar for more useful packages.

Installation

From the Julia REPL, type ] to enter the Pkg REPL mode and run

pkg> add ArviZ

Usage

See the Quickstart for example usage and the API Overview for description of functions.

Extending ArviZ.jl

To use a custom data type with ArviZ.jl, simply overload InferenceObjects.convert_to_inference_data to convert your input(s) to an InferenceObjects.InferenceData.