PaddedViews provides a simple wrapper type, PaddedView, to add "virtual" padding to any array without copying data. Edge values not specified by the array are assigned a fillvalue. Multiple arrays may be "promoted" to have common indices using the paddedviews function.

This package introduces the type StructArray which is an AbstractArray whose elements are struct (for example NamedTuples, or ComplexF64, or a custom user defined struct). While a StructArray iterates structs, the layout is column based (meaning each field of the struct is stored in a separate Array).

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 is for calculating derivatives, gradients, Jacobians, Hessians, etc. numerically. This library is for maximizing speed while giving a usable interface to end users in a way that specializes on array types and sparsity.

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.

ImageInTerminal.jl is a drop-in package that once imported changes a how a single Colorant and whole Colorant arrays (i.e. Images) are displayed in the interactive REPL. The displayed images will be downscaled to fit into the size of your active terminal session.

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

A Julia package to contain non-standard matrix factorizations. At the moment it implements the QL, RQ, and UL factorizations, a combined Cholesky factorization with inverse, and polar decompositions. In the future it may include other factorizations such as the LQ factorization.

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.

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

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.

JSON.jl is a pure Julia module which supports parsing and printing JSON documents.

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

This package provides the type DataValue that is used to represent missing data.

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

This library provides tools for working with Julia code and expressions. This includes a template-matching system and code-walking tools that let you do deep transformations of code.

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

This package only contains and exports a single function realdot(x, y). It computes real(LinearAlgebra.dot(x, y)) while avoiding computing the imaginary part of LinearAlgebra.dot(x, y) if possible. The real dot product is useful when one treats complex numbers as embedded in a real vector space.

This package exports following operations over bit vectors with extremely fast speed while keeping extra memory usage small:

• getindex(bv::IndexableBitVectors, i::Integer): i-th element of bv

• rank(b::Bool, bv::AbstractIndexableBitVector, i::Integer): the number of occurrences of bit b in bv[1:i]

• select(b::Bool, bv::AbstractIndexableBitVector, i::Integer): the index of i-th occurrence of b in bv.

and other shortcuts or types.

This package provides a collection of useful extensions for Julia's built-in docsystem. These are features that are not yet mature enough to be considered for inclusion in Base, or that have sufficiently niche use cases that including them with the default Julia installation is not seen as valuable enough at this time.

This package provides consistent and extensible functional programming infrastructures, and metaprogramming facilities.

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.

Tracker.jl previously provided Flux.jl with automatic differentiation for its machine learning platform.

This package contains the testset from Julia, packaged into a loadable module.