This Julia package provides several utilities for working with tree-like data structures. Most importantly, it defines the children method that any package that contains such a data structure may import and extend in order to take advantage of any generic tree algorithm in this package.

This package provides a set of custom string types of various fixed sizes. Each inline string is a custom primitive type and can benefit from being stack friendly by avoiding allocations/heap tracking in the GC. When used in an array, the elements are able to be stored inline since each one has a fixed size. Currently support inline strings from 1 byte up to 255 bytes.

This package provides a summary of available CPU features in Julia.

This package implements a variety of interpolation schemes for the Julia language. It has the goals of ease-of-use, broad algorithmic support, and exceptional performance.

This package provides types similar to Julia's Rational type, which make some sacrifices but have better computational performance.

This package defines functionality to calculate volume element changes for functions that perform a change of variables (like coordinate transformations).

This package provides support for image resizing, image rotation, and other spatial transformations of arrays.

This package provides a macro-based implementation of traits. The main idea behind traits is to group types outside the type-hierarchy and to make dispatch work with that grouping. The difference to Union-types is that types can be added to a trait after the creation of the trait, whereas Union types are fixed after creation.

Graphics.jl is an abstraction layer for graphical operations in Julia.

This package allows programmers to explicitly SIMD-vectorize their Julia code. By exposing SIMD vector types and corresponding operations, the programmer can explicitly vectorize their code. While this does not guarantee that the generated machine code is efficient, it relieves the compiler from determining whether it is legal to vectorize the code, deciding whether it is beneficial to do so, and rearranging the code to synthesize vector instructions.

This package implements a variety of data structures, including, CircularBuffer, Queue, Stack, Accumulators, LinkedLists, SortedDicts and many others.

This package will provide a library of functions useful for machine learning, such as softmax, sigmoid, convolutions and pooling. It doesn't provide any other "high-level" functionality like layers or AD.

Minimal package which enables to add custom gradients to Zygote, without depending on Zygote itself.

This package provides a type stable and efficient wrapper of arbitrary functions.

The Preferences package provides an integrated way for packages to store configuration switches to persistent TOML files, and use those pieces of information at both run time and compile time in Julia. This enables the user to modify the behavior of a package, and have that choice reflected in everything from run time algorithm choice to code generation at compile time.

Quaternions are best known for their suitability as representations of 3D rotational orientation. They can also be viewed as an extension of complex numbers.

This package provides a collection of useful bit-twiddling tricks, ready to use as functions, with detailed documentation and example real-world use cases.

This package implements handy macros @recipe and @series which will define a custom transformation and attach attributes for user types. Its design is an attempt to simplify and generalize the summary and display of types and data from external packages. With this package it is possible to describe visualization routines that can be used as components in more complex visualizations.

The package provides a light-weight dependency for defining sensitivities for functions without the need to depend on ChainRules itself.

This package provides tools for working with the basic calculus operations of differentiation and integration. The Calculus package produces approximate derivatives by several forms of finite differencing or produces exact derivative using symbolic differentiation. It can also be used to compute definite integrals by different numerical methods.

This package provides special mathematical functions, including Bessel, Hankel, Airy, error, Dawson, exponential (or sine and cosine) integrals, eta, zeta, digamma, inverse digamma, trigamma, and polygamma functions.

This package provides several functions to manipulate strings with ANSI escape sequences.

This package is for calculating derivatives, gradients, Jacobians, Hessians, etc. numerically. This library is for maximizing speed while giving a usable interface to end users in a way that specializes on array types and sparsity.

FuzzyCompletions provides fuzzy completions for a Julia runtime session.