The purpose of this package is to allow Julia function calls to be temporarily overloaded for the purpose of testing.

The SortingAlgorithms package provides three sorting algorithms that can be used with Julia's standard sorting API: heapsort, timsort and radixsort.

This package defines functions for getting multiple indices out of dictionaries, tuples, etc, extending this ability beyond AbstractArray.

This package takes a string or buffer containing Julia code, performs lexical analysis and returns a stream of tokens.

This package provides a wrapper around ImageMagick version 6. It was split off from Images.jl to make image I/O more modular.

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.

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

An IndirectArray is one that encodes data using a combination of an index and a value table. Each element is assigned its own index, which is used to retrieve the value from the value table. Among other uses, IndirectArrays can represent indexed images, sometimes called "colormap images" or "paletted images."

This package provides the ability to directly call and fully interoperate with Python from the Julia language. You can import arbitrary Python modules from Julia, call Python functions (with automatic conversion of types between Julia and Python), define Python classes from Julia methods, and share large data structures between Julia and Python without copying them.

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 the @cse macro, which performs common subexpression elimination.

This package make loading packages faster, maybe. It supports specifying glue code in packages which will load automatically when another package is loaded, so that explicit dependencies (and long load times) can be avoided.

This package provides macros for defining the required behaviours of Julia interfaces, and stating that an object implements them.

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

This package provides Julia implementations of the Standard Clausen functions and Glaisher-Clausen functions of integer order for real or complex arguments.

This is a small package to make it easier to type LaTeX equations in string literals in the Julia language.

When visualizing images, it is not uncommon to provide a 2D view of different image sources. For example, comparing multiple images of different sizes, getting a preview of machine learning dataset. This package aims to provide easy-to-use tools for such tasks.

This package provides a fast, extensible progress bar for Julia. This can help users track the progress of long-running tasks.

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 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.

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 implements a trait-based framework for describing array layouts such as column major, row major, etc. that can be dispatched to appropriate BLAS or optimised Julia linear algebra routines. This supports a much wider class of matrix types than Julia's in-built StridedArray.

This package provides an alternative interface for dictionaries in Julia, for improved productivity and performance.

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