Julia implementation of Data structures

Root finding functions for Julia

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

Uniform Interface for positive definite matrices of various structures

Fixed point types for julia

Julia macro for re-exporting one module from another

Fast, continuous interpolation of discrete datasets in Julia

Compatibility across Julia versions

Lazy code loading for Julia

Support for the Woodbury matrix identity for Julia

Basic color definitions and traits

Efficient filtering and linear algebra routines for multidimensional arrays

Faster Rational-like types for Julia

Simple Traits for Julia

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

Treat colors as if they are n-vectors for the purposes of arithmetic

Color manipulation utilities for Julia

Physical quantities with arbitrary units

Statically sized arrays for Julia

Special mathematical functions in Julia

Lazy in-place transformations of arrays

Package-specific AbstractUnitRange types for julia

Julia types for representing images

Extensions for Julia's docsystem.

Julia package for selecting hardware, libraries, and algorithms for a computation

Julia package for indices-aware array concatenation and growth

Julia implementations of multidimensional array convolution and nonlinear stencil operations

Julia package for fast fourier transforms and periodic views

Julia package to facilitate writing mulithreaded, multidimensional, cache-efficient code

Add virtual padding to the edges of an array

Julia bindings to the FFTW library for fast Fourier transforms

A Julia framework for implementing FFTs

Julia package for lazily representing matrices filled with a single entry

Julia package for lazily viewing a 3D or 4D array as an expanded 2D array in the form of a mosaic of matrix slices

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Julia implementation of associative containers that preserve insertion order

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

A common solve function for scientific machine learning (SciML) and beyond

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.

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