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.

The VersionParsing package implements flexible parsing of version-number strings into Julia's built-in VersionNumber type, via the vparse(string) function. Unlike the VersionNumber(string) constructor, vparse(string) can handle version-number strings in a much wider range of formats than are encompassed by the semver standard. This is useful in order to support VersionNumber comparisons applied to "foreign" version numbers from external packages.

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

This package contains generic helper algorithms for building plotting components.

Astronomical https://www.atnf.csiro.au/people/mcalabre/WCS/ library for Julia. This package wraps the WCSLIB C library.

This package supports representing banded matrices by only the entries on the bands.

FuzzyCompletions provides fuzzy completions for a Julia runtime session.

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

This package provides various examples.

This package provides a Julia interface to the Matplotlib plotting library from Python, and specifically to the matplotlib.pyplot module. PyPlot uses the Julia PyCall package to call Matplotlib directly from Julia with little or no overhead (arrays are passed without making a copy).

This package implements methods to take derivatives, gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really) using forward mode automatic differentiation (AD).

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.

MbedTLS.jl provides a wrapper around the mbed TLS and cryptography C library for Julia.

This package provides a multivariate polynomials implementation of commutative and non-commutative variables.

This package is an implementation of tropical (min-plus) arithmetic in Julia.

This package provides a simple and flexible IR format, expressive enough to work with both lowered and typed Julia code, as well as external IRs. It can be used with Julia metaprogramming tools such as Cassette.

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.

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."

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

This Julia package provides the adapt(T, x) function acts like convert(T, x), but without the restriction of returning a T. This allows you to "convert" wrapper types like Adjoint to be GPU compatible without throwing away the wrapper.

FilePathsBase.jl provides a type based approach to working with filesystem paths in Julia.

This package provides string distances in Julia. Distances are defined for AbstractStrings, and any iterator that define length(). The package also defines Distance "modifiers" that can be applied to any distance.

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

This package provides a Julia interface defining a collection of types (without instances) for implementing conventions about the scientific interpretation of data. This package makes a distinction between the machine type and the scientific type of a Julia object. A machine type refers to the Julia type being used to represent the object, for instance Float64. The scientific type refers to how the object should be interpreted, for instance Continuous or Multiclass3.