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.

ImageCore is the lowest-level component of the system of packages designed to support image processing and computer vision.

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

BufferedStreams.jl provides buffering for IO operations. It can wrap any IO type automatically making incremental reading and writing faster.

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 a combinatorics library for Julia, focusing mostly (as of now) on enumerative combinatorics and permutations.

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

This package provides a parser for Julia code.

This package generates formatted output from timings made in different sections of a program.

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

Extents.jl is a small package that defines an Extent object that can be used by the different Julia spatial data packages. Extent is a wrapper for a NamedTuple of tuples holding the lower and upper bounds for each dimension of a object.

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

Crayons is a package that makes it simple to write strings in different colors and styles to terminals. It supports the 16 system colors, both the 256 color and 24 bit true color extensions, and the different text styles available to terminals.

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.

This package offers Python-style general formatting and c-style numerical formatting.

The goal of RangeArrays is to provide efficient and convenient array data structures where the columns of the arrays are generated (on the fly) by Ranges.

This package provides an interface for manipulating multivariate polynomials. Implementing algorithms on polynomials using this interface will allow the algorithm to work for all polynomials implementing this interface. The interface contains functions for accessing the coefficients, monomials, defining arithmetic operations on them, rational functions, division with remainder, calculus and differentiation, and evaluation and substitution.

TableTraits defines a generic interface for tabular data.

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

This package intends to provide a simple RNG with stable streams, suitable for tests in packages which need reproducible streams of random numbers across Julia versions. Indeed, the Julia RNGs provided by default are documented to have non-stable streams (which for example enables some performance improvements).

The purpose of this package is partly to extend linear algebra functionality in base to cover generic element types, e.g. BigFloat and Quaternion, and partly to be a place to experiment with fast linear algebra routines written in Julia (except for optimized BLAS).

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.

This minimalistic package serves as the foundation for other SIMD packages in Julia.