Powerful convenience for Julia visualizations and data analysis

In-memory tabular data in Julia

Package to call Python functions from the Julia language

Optimization functions for Julia

Forward Mode Automatic Differentiation for Julia

Statically sized arrays for Julia

Macro(s) for vectorizing loops.

Progress meter for long-running computations

Julia implementation of Data structures

Formatted output of timed sections in Julia

HTTP for Julia

A benchmarking framework for the Julia language

Physical quantities with arbitrary units

Basic statistics for Julia

Convert julia objects to LaTeX equations, arrays or other environments.

HDF5-compatible file format in pure Julia

Fast, continuous interpolation of discrete datasets in Julia

Utility library for working with CSV and other delimited files in the Julia programming language

An optimized graphs package for the Julia programming language

Julia bindings for the Enzyme automatic differentiator

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

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

High performance nearest neighbor data structures (KDTree and BallTree) and algorithms for Julia.

Print data in formatted tables.

Julia functional programming infrastructures and metaprogramming facilities

A data structure for mathematical optimization problems

Save and load data in the HDF5 file format from Julia

MPI wrappers for Julia

Plotting for Julia based on GR, a framework for visualisation applications

Special mathematical functions in Julia

Root finding functions for Julia

A Julia Basket of Hand-Picked Krylov Methods

Reusable array functionality for Julia's various GPU backends.

JSON parsing and printing

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

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

Lazy arrays and linear algebra in Julia

An interface for tables in Julia

Adaptive 1d numerical Gauss–Kronrod integration 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.