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.

ReferenceTests.jl is a Julia package that adds a couple of additional macros to your testing toolbox. In particular, it focuses on functionality for testing values against reference files, which in turn the package can help create and update if need be.

Various special functions based on log and exp moved from StatsFuns.jl into a separate package, to minimize dependencies. These functions only use native Julia code, so there is no need to depend on librmath or similar libraries.

This package provides a collection of colorschemes.

This package provides a display system which enables the user handle multiple input/output devices and decide what media types get displayed where.

This package for the Julia language provides an array type (the AxisArray) that knows about its dimension names and axis values. This allows for indexing by name without incurring any runtime overhead. This permits one to implement algorithms that are oblivious to the storage order of the underlying arrays. AxisArrays can also be indexed by the values along their axes, allowing column names or interval selections.

A Julia package for evaluating distances(metrics) between vectors. This package also provides optimized functions to compute column-wise and pairwise distances, which are often substantially faster than a straightforward loop implementation.

This package provides SentinelArrayT that wraps an AbstractArray of type T, and accepts a sentinel and value argument.

This package is an add-on to ColorTypes.jl and provides fast mathematical operations for objects with types such as RGB and Gray. Specifically, with this package both grayscale and RGB colors are treated as if they are points in a normed vector space.

This package enables the Julia compiler to generate efficient code when running test cases. Test cases are typically run with flags that prevent efficient code generation. This package detects those flags and instead spawns a separate Julia process without the flags in which to run the test cases.

This package provides an implementation of an associative container mapping (K,V) pairs via the type IntervalTreeK, V. The type K may be any ordered type.

This package aims to offer a standard set of Geometry types, which easily work with metadata, query frameworks on geometries and different memory layouts. The aim is to create a solid basis for Graphics/Plotting, finite elements analysis, Geo applications, and general geometry manipulations - while offering a Julian API, that still allows performant C-interop.

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.

This package provides information about the features of the host CPU in Julia.

This package provides support for one-dimensional numerical integration in Julia using adaptive Gauss-Kronrod quadrature. The code was originally part of Base Julia. It supports integration of arbitrary numeric types, including arbitrary precision (BigFloat), and even integration of arbitrary normed vector spaces (e.g. matrix-valued integrands).

This package provides a pooled representation of arrays for purposes of compression when there are few unique elements.

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

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

ArnoldiMethod.jl provides an iterative method to find a few approximate solutions to the eigenvalue problem in standard form with main goals:

• Having a native Julia implementation of the eigs function that performs as well as ARPACK. With native we mean that its implementation should be generic and support any number type. Currently the partialschur function does not depend on LAPACK, and removing the last remnants of direct calls to BLAS is in the pipeline.

• Removing the dependency of the Julia language on ARPACK. This goal was already achieved before the package was stable enough, since ARPACK moved to a separate repository Arpack.jl.

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 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.

This package implements real and complex polylogarithms, including the real and complex dilogarithm and trilogarithm in Julia.

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

Static.jl defines a limited set of statically parameterized types and a common interface that is shared between them.