Forward Mode Automatic Differentiation for Julia

Statically sized arrays for Julia

Macro(s) for vectorizing loops.

Save and load data in the HDF5 file format from Julia

MPI wrappers for Julia

Special mathematical functions in Julia

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.

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.

Reduce time-to-first-execution of Julia code

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

Lazy code loading for Julia

Julia package for lazily representing matrices filled with a single entry

Basic Geometry Types

Julia macro for re-exporting one module from another

Common functional iterator patterns

Julia package for writing VTK XML files

Finite Element tools in Julia

Compatibility across Julia versions

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

Julia wrapper for the LLVM C API

Project Preferences Package

Extensions for Julia's docsystem.

Fast parsing machinery for basic types in Julia

Uniform Interface for positive definite matrices of various structures

Julia implementation of associative containers that preserve insertion order

A light-weight Julia package for XML based on libxml2.

A Julia Protocol for Geospatial Data

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SparseArrays.jl is a Julia stdlib

Simple, consistent interfaces for any codec.

`@pack!` and `@unpack` macros

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.

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