This package provides a collection of useful extensions for Julia's built-in docsystem. These are features that are not yet mature enough to be considered for inclusion in Base, or that have sufficiently niche use cases that including them with the default Julia installation is not seen as valuable enough at this time.

JSON.jl is a pure Julia module which supports parsing and printing JSON documents.

This package provides tools to express a design pattern for dealing with large/ nested structures, as in machine learning and optimisation. For large machine learning models it can be cumbersome or inefficient to work with parameters as one big, flat vector, and structs help in managing complexity; but it is also desirable to easily operate over all parameters at once, e.g. for changing precision or applying an optimiser update step.

This package defines the BFloat16 data type. The only currently available hardware implementation of this datatype are Google's Cloud TPUs. As such, this package is suitable to evaluate whether using TPUs would cause precision problems for any particular algorithm, even without access to TPU hardware. Note that this package is designed for functionality, not performance, so this package should be used for precision experiments only, not performance experiments.

This package provides the type DataValue that is used to represent missing data.

BenchmarkTools.jl makes performance tracking of Julia code easy by supplying a framework for writing and running groups of benchmarks as well as comparing benchmark results.

This package provides some common helper functions that make it easier to implement various Base.show functions for types that participate in the TableTraits.jl ecosystem.

This package provides a simple Julian API to use the libsass library to compile scss and sass files to css.

This package provides support for the Woodbury matrix identity for the Julia programming language. This is a generalization of the Sherman-Morrison formula. Note that the Woodbury matrix identity is notorious for floating-point roundoff errors, so be prepared for a certain amount of inaccuracy in the result.

Zygote provides source-to-source automatic differentiation (AD) in Julia, and is the next-generation AD system for the Flux differentiable programming framework.

MutableArithmetics is a Julia package which allows:

• mutable types to implement mutable arithmetics

• algorithms that could exploit mutable arithmetics to exploit them while still being completely generic

This package is the counterpart of AbstractArray interface, but for GPU array types. It provides functionality and tooling to speed-up development of new GPU array types. This package is not intended for end users; instead, you should use one of the packages that builds on GPUArrays.jl, such as CUDA.jl, oneAPI.jl or AMDGPU.jl.

This package only contains and exports a single function realdot(x, y). It computes real(LinearAlgebra.dot(x, y)) while avoiding computing the imaginary part of LinearAlgebra.dot(x, y) if possible. The real dot product is useful when one treats complex numbers as embedded in a real vector space.

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

This package enables the Markdown / MkDocs backend of Documenter.jl.

This library generalizes and unifies the notion of measures used in Compose, Compose3D, and Escher. It allows building up and representing expressions involving differing types of units that are then evaluated, resolving them into absolute units.

This package just exports one type: the InvertedIndex, or Not for short. It can wrap any supported index type and may be used as an index into any AbstractArray subtype, including OffsetArrays.

HTTP.jl is a Julia library for HTTP Messages, implementing both a client and a server.

Measurements.jl is an error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration. The linear error propagation theory is employed to propagate the errors.

The DualNumbers Julia package defines the Dual type to represent dual numbers, and supports standard mathematical operations on them. Conversions and promotions are defined to allow performing operations on combinations of dual numbers with predefined Julia numeric types.

ANSIColoredPrinters.jl converts a text qualified by ANSI escape codes to another format.

This package allows you to reduce the latency of the first execution of Julia code. It is applicable to both package developers and end users in their personal workflows.

This package defines a new operator for composition of morphisms.

The @unpack and @pack! macros work to unpack types, modules, and dictionaries.