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.

IteratorInterfaceExtensions defines a small number of extensions to the iterator interface.

This package provides a namespace for data-related generic function definitions to solve the optional dependency problem; packages wishing to share and/or extend functions can avoid depending directly on each other by moving the function definition to DataAPI.jl and each package taking a dependency on it.

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 an alternative interface for dictionaries in Julia, for improved productivity and performance.

BenchmarkTools.jl makes performance tracking of Julia code easy by supplying a framework for writing and running groups of benchmarks as well as comparing benchmark results.

This package provides the StructTypes.StructType trait for Julia types to declare the kind of "struct" they are, providing serialization/deserialization packages patterns and strategies to automatically construct objects.

MutableArithmetics is a Julia package which allows:

• mutable types to implement mutable arithmetics

• algorithms that could exploit mutable arithmetics to exploit them while still being completely generic

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

This package provides a wrapper around ImageMagick version 6. It was split off from Images.jl to make image I/O more modular.

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.

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.

The Schur decomposition is the workhorse for eigensystem analysis of dense matrices. The diagonal eigen-decomposition of normal (especially Hermitian) matrices is an important special case, but for non-normal matrices the Schur form is often more useful.

This package implements handy macros @recipe and @series which will define a custom transformation and attach attributes for user types. Its design is an attempt to simplify and generalize the summary and display of types and data from external packages. With this package it is possible to describe visualization routines that can be used as components in more complex visualizations.

This package takes a string or buffer containing Julia code, performs lexical analysis and returns a stream of tokens.

This package make loading packages faster, maybe. It supports specifying glue code in packages which will load automatically when another package is loaded, so that explicit dependencies (and long load times) can be avoided.

This package provides Data types and methods for common operations with biological sequences, including DNA, RNA, and amino acid sequences.

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.

This package provides a function extrapolate that extrapolates a given function f(x) to f(x0), evaluating f only at a geometric sequence of points > x0 (or optionally < x0). The key algorithm is Richardson extrapolation using a Neville–Aitken tableau, which adaptively increases the degree of an extrapolation polynomial until convergence is achieved to a desired tolerance (or convergence stalls due to e.g. floating-point errors). This allows one to obtain f(x0) to high-order accuracy, assuming that f(x0+h) has a Taylor series or some other power series in h.

This package compiles regular expressions into Julia code, which is then compiled into low-level machine code by the Julia compiler. The package is designed to generate very efficient code to scan large text data, which is often much faster than handcrafted code. Automa.jl can insert arbitrary Julia code that will be executed in state transitions. This makes it possible, for example, to extract substrings that match a part of a regular expression.

This small package supports the representation of images as AxisArrays to endow the axes with "meaning," and makes programming with such arrays easy via traits.

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

The is package provides a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives.

This package allows you to reduce the latency of the first execution of Julia code. It is applicable to both package developers and end users in their personal workflows.