Package to call Python functions from the Julia language

Forward Mode Automatic Differentiation for Julia

Python and Julia in harmony.

Statically sized arrays for Julia

Macro(s) for vectorizing loops.

A benchmarking framework for the Julia language

A Julia package for evaluating distances (metrics) between vectors.

Types with default field values, keyword constructors and (un-)pack macros

Special mathematical functions in Julia

Julia solvers for systems of nonlinear equations and mixed complementarity problems

JSON parsing and printing

MacroTools provides a library of tools for working with Julia code and expressions.

An interface for tables in Julia

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.

Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

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Reduce time-to-first-execution of Julia code

Lazy code loading for Julia

Fortran-like arrays with arbitrary, zero or negative starting indices.

Conda managing Julia binary dependencies

Julia macro for re-exporting one module from another

Compatibility across Julia versions

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

Project Preferences Package

Add Conda dependencies to your Julia project

Line search methods for optimization and root-finding

Fast parsing machinery for basic types in Julia

Extensions for Julia's docsystem.

Julia implementation of associative containers that preserve insertion order

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SparseArrays.jl is a Julia stdlib

`@pack!` and `@unpack` macros

Abstract definitions and convenience methods for describing, processing, and constructing Julia objects

A simple shared suite of common derivative definitions

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

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

Scratch spaces for all your persistent mutable data needs

Ask the CPU for cache sizes, SIMD feature support, a running hypervisor, and more.

Julia math built-ins which return NaN and accumulator functions which ignore NaN