Line search methods for optimization and root-finding

Fast parsing machinery for basic types in Julia

Extensions for Julia's docsystem.

Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes

Mike's Little Intermediate Representation

Utilities and abstractions for Machine Learning tasks

Uniform Interface for positive definite matrices of various structures

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Package for checking function arguments

Functions generated at runtime without world-age issues or overhead

Random Number Generators for the Julia Language.

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

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.

Julia implementation of associative containers that preserve insertion order

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Julia-based implementation of ellipsis array indexing notation `..`

SparseArrays.jl is a Julia stdlib

This is what I wish `Base.@enum` was.

Simple, consistent interfaces for any codec.

Cunning tricks though the julia compiler internals

Collective tools for metaprogramming on Julia Expr

`@pack!` and `@unpack` macros

Some utilities for working with NamedTuples

Fixed point types for julia

TAR files: create, list, extract them in pure Julia

Basic color definitions and traits

Light-weight expression manipulation tools

🏩🏠🌆🏨🌇🏦

A simple shared suite of common derivative definitions

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Julia package for various special functions based on `log` and `exp`.

Optimisers.jl defines many standard optimisers and utilities for learning loops.

Missing value support for Julia

A Julia package for calculating hypergeometric functions

Base library providing vectorization-tools (ie, SIMD) that other libraries are built off of.

A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)

Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.