Print data in formatted tables.

No-overhead breakpoints in Julia

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

Overdub Your Julia Code

Heterogeneous programming in Julia

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

Special mathematical functions in Julia

Reverse Mode Automatic Differentiation for Julia

A Julia Basket of Hand-Picked Krylov Methods

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

Julia solvers for systems of nonlinear equations and mixed complementarity problems

Efficient implementation of struct arrays in Julia

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

Polynomial manipulations in Julia

Lazy arrays and linear algebra in Julia

An interface for tables in Julia

Julia package for Gaussian quadrature

Arrays with arbitrarily nested named components.

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

Saving and loading julia variables while preserving native types

Julia bindings to the FFTW library for fast Fourier transforms

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.

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

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

LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.

The cheapest threads you can find!

Fast jacobian computation through sparsity exploitation and matrix coloring

Mathematical functions related to statistics.

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

Main Package for IO, loading all different kind of files

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A combinatorics library for Julia

A library of data interpolation and smoothing functions