Abstract types and interfaces for Markov chain Monte Carlo methods

Common types and interfaces for probabilistic programming

Abstract julia interfaces for working with trees

Update immutable data

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Repository for automatic differentiation backend types

An efficient sampler for discrete random variables

ANSI escape code translator

Package for checking function arguments

Tools for writing functions that handle many kinds of IO arguments

The Arnoldi Method with Krylov-Schur restart, natively in Julia.

Immutables as mutables, mutables as immutables.

Base API optimized for tuples

Implementation of normalising flows and constrained random variable transformations

C-compatible enum for Julia

Forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.

Interface for transformation functions in Julia

Zlib codecs for TranscodingStreams.jl.

A common solve function for scientific machine learning (SciML) and beyond

Compatibility across Julia versions

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Primitives for construction of objects

A data-focused namespace for packages to share functions

Julia implementation of Data structures

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Interface for mathematical/statistical densities in Julia

Create and control multiple Julia processes remotely for distributed computing. Ships as a Julia stdlib.

A Julia package for probability distributions and associated functions.

Extensions for Julia's docsystem.

A documentation generator for Julia.

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Implementation of domain-specific language (DSL) for dynamic probabilistic programming

Julia bindings for the Enzyme automatic differentiator

Light-weight expression manipulation tools

Main Package for IO, loading all different kind of files

Julia package for lazily representing matrices filled with a single entry

Parameterise all the things