A Julia framework for implementing FFTs

Abstract types and interfaces for Markov chain Monte Carlo methods

Abstract julia interfaces for working with trees

Update immutable data

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Repository for automatic differentiation backend types

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

An efficient sampler for discrete random variables

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.

Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations

Astronomical time keeping in Julia

A julia code generator for regular expressions

Efficient filtering and linear algebra routines for multidimensional arrays

Performant arrays where each dimension can have a named axis with values

Immutables as mutables, mutables as immutables.

Base API optimized for tuples

Basic (+chebyshev) interpolation recipes in Julia

A benchmarking framework for the Julia language

Bijection datatype for Julia.

Implementation of normalising flows and constrained random variable transformations

BitFlag.jl provides an Enum-like type for bit flag option values.

C-compatible enum for Julia

C-style interface to the libcfitsio library.

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

A bzip2 codec for TranscodingStreams.jl.

Zlib codecs for TranscodingStreams.jl.

Color manipulation utilities for Julia

Colorschemes, colormaps, gradients, and palettes

Basic color definitions and traits

Treat colors as if they are n-vectors for the purposes of arithmetic

A combinatorics library for Julia

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

Naïve combined subexpression elimination in Julia

Fixing the world one invalidator at a time.

Compatibility across Julia versions

A common interface for composite types, which may consist of several components