Common functional iterator patterns (formerly Iterators.jl).

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.

URIs.jl is a Julia package that allows parsing and working with URIs, as defined in RFC 3986.

This package is designed to help in testing ChainRulesCore.frule and ChainRulesCore.rrule methods. The main entry points are ChainRulesTestUtils.frule_test, ChainRulesTestUtils.rrule_test, and ChainRulesTestUtils.test_scalar. Currently this is done via testing the rules against numerical differentiation (using FiniteDifferences.jl).

ChainRulesTestUtils.jl is separated from ChainRulesCore.jl so that it can be a test-only dependency, allowing it to have potentially heavy dependencies, while keeping ChainRulesCore.jl as light-weight as possible.

This package provides the @muladd macro. It automatically converts expressions with multiplications and additions or subtractions to calls with muladd which then fuse via FMA when it would increase the performance of the code. The @muladd macro can be placed on code blocks and it will automatically find the appropriate expressions and nest muladd expressions when necessary. In mixed expressions summands without multiplication will be grouped together and evaluated first but otherwise the order of evaluation of multiplications and additions is not changed.

This package provides tools to help you develop code. Juno is built on the Atom text editor. Juno consists of both Julia and Atom packages in order to add Julia-specific enhancements, such as syntax highlighting, a plot pane, integration with Julia's debugger, a console for running code, and much more.

Consider that the package is “maintenance-only mode” and only receives bug fixes. The Julia IDE effort is pointed to extension for VSCode.

This package contains utilities for setting up documentation generation with Documenter.jl.

MsgPack.jl is a MessagePack implementation in pure Julia, with type-driven, overloadable packing/unpacking functionality.

Tracker.jl previously provided Flux.jl with automatic differentiation for its machine learning platform.

This package provides another JSON package for Julia, with a focus on speed and slick struct mapping.

This package provides alignment algorithms and data structures for sequence of DNA, RNA, and amino acid sequences.

Configurations.jl provides a macro @option to let you define structs to represent options/configurations, and serialize between different option/configuration file formats such as TOML.

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

This package aims at establishing common ground for Optim.jl, LineSearches.jl, and NLsolve.jl. The common ground is mainly the types used to hold objective related callables, information about the objectives, and an interface to interact with these types.

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

This package compiles regular expressions into Julia code, which is then compiled into low-level machine code by the Julia compiler. The package is designed to generate very efficient code to scan large text data, which is often much faster than handcrafted code. Automa.jl can insert arbitrary Julia code that will be executed in state transitions. This makes it possible, for example, to extract substrings that match a part of a regular expression.

PositiveFactorizations is a package for computing a positive definite matrix decomposition (factorization) from an arbitrary symmetric input. The motivating application is optimization (Newton or quasi-Newton methods), in which the canonical search direction -H/g (H being the Hessian and g the gradient) may not be a descent direction if H is not positive definite.

This package provides a C-compatible enum for Julia.

This package provides a canonical set of default initial values and identity elements for Julia.

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

TypedTables.jl provides two column-based storage containers: Table and FlexTable, both of which represent an array of NamedTuples. This package is designed to be lightweight, easy-to-use and fast, and presents a very minimal new interface to learn.

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 is a Julia interface to libquadmath, providing a Float128 type corresponding to the IEEE754 binary128 floating point format.

The Tables.jl package provides simple, yet powerful interface functions for working with all kinds tabular data.