Fast jacobian computation through sparsity exploitation and matrix coloring

Mathematical functions related to statistics.

A bridge between Julia and the Web.

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications

Convenient input and display of LaTeX equation strings for the Julia language

Reduce time-to-first-execution of Julia code

Color manipulation utilities for Julia

Abstract julia interfaces for working with trees

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

Lazy code loading for Julia

BlockArrays for Julia

Colorschemes, colormaps, gradients, and palettes

Reactive programming primitives for Julia

Julia package for lazily representing matrices filled with a single entry

Update immutable data

Conda managing Julia binary dependencies

Update deeply nested immutable structs.

An interface to various automatic differentiation backends in Julia.

Julia macro for re-exporting one module from another

Interpreter for Julia code

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A WebSockets library for Julia

Julia package for 1-d and 2-d splines

Reusable compiler infrastructure for Julia GPU backends.

Simple Traits for Julia

An enhancement to julia piping syntax

Compatibility across Julia versions

Composable Loggers for the Julia Logging StdLib

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

It's editing-time, do you know where your methods are?

The Base interface of the SciML ecosystem

Julia wrapper for the LLVM C API

Easy benchmark tracking for packages

Project Preferences Package

A Julia package for representing banded matrices

A Julia framework for implementing FFTs

Functional and persistent data structures for Julia

Themes for the Julia plotting package Plots.jl

Line search methods for optimization and root-finding