This package allows you to query the availability of specific CPU features with low run-time cost.

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 will provide a library of functions useful for machine learning, such as softmax, sigmoid, convolutions and pooling. It doesn't provide any other "high-level" functionality like layers or AD.

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.

libuv is a multi-platform support library with a focus on asynchronous I/O. Among other things, it supports event loops via epoll, kqueue, and similar IOCP, and event ports, asynchronous TCP/UDP sockets, asynchronous DNS resolution, asynchronous file system operations, and threading primitives.

This package provides an R interface to Julia', which is a high-level, high-performance dynamic programming language for numerical computing, see <https://julialang.org/> for more information. It provides a high-level interface as well as a low-level interface. Using the high level interface, you could call any Julia function just like any R function with automatic type conversion. Using the low level interface, you could deal with C-level SEXP directly while enjoying the convenience of using a high-level programming language like Julia'.

This package provides a wrapper for the GR framework.

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

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

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

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

This package provides types similar to Julia's Rational type, which make some sacrifices but have better computational performance.

This package provides a convenient function form of the conditional ifelse. It is similar to Core.ifelse but it is extendable.

This package provides Julia implementation for reading and writing FITS files, based on the cfitsio library.

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 is made to be included into packages that just need the ffmpeg binaries + executables, and don't want the overhead of VideoIO.jl.

This package provides a wide array of functions for dealing with color. This includes conversion between colorspaces, measuring distance between colors, simulating color blindness, parsing colors, and generating color scales for graphics.

The Compat package is designed to ease interoperability between older and newer versions of the Julia language. The Compat package provides a macro that lets you use the latest syntax in a backwards-compatible way.

Images.jl is increasingly becoming an "umbrella package" that exports a set of packages which are useful for common image processing tasks. Most of these packages are hosted at JuliaImages, JuliaArrays, JuliaIO, JuliaGraphics, and JuliaMath.

PDMats.jl supports efficient computation on positive definite matrices of various structures. In particular, it provides uniform interfaces to use positive definite matrices of various structures for writing generic algorithms, while ensuring that the most efficient implementation is used in actual computation.

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 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).

FileIO aims to provide a common framework for detecting file formats and dispatching to appropriate readers/writers. The two core functions in this package are called load and save, and offer high-level support for formatted files (in contrast with Julia's low-level read and write).

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.