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 is a small package to make it easier to type LaTeX equations in string literals in the Julia language.

This package provides zlib codecs for TranscodingStreams.jl.

The SortingAlgorithms package provides three sorting algorithms that can be used with Julia's standard sorting API: heapsort, timsort and radixsort.

This package provides representations for infinity and negative infinity in Julia.

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

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 aims to offer a standard set of Geometry types, which easily work with metadata, query frameworks on geometries and different memory layouts. The aim is to create a solid basis for Graphics/Plotting, finite elements analysis, Geo applications, and general geometry manipulations - while offering a Julian API, that still allows performant C-interop.

This package provides a functionality to calculate Earth orientation parameters with data retrieved from IERS.

This package provides definitions for common functions that are useful for symbolic expression manipulation in Julia. Its purpose is to provide a shared interface between various symbolic programming packages, for example SymbolicUtils.jl, Symbolics.jl, and Metatheory.jl.

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

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 tools to help you develop code. Juno is built on the Atom text editor. Juno consists of both Julia and Atom packages in order to add Julia-specific enhancements, such as syntax highlighting, a plot pane, integration with Julia's debugger, a console for running code, and much more.

Consider that the package is “maintenance-only mode” and only receives bug fixes. The Julia IDE effort is pointed to extension for VSCode.

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.

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 provides primitive differentiation rules that can be composed via various formulations of the chain rule. Using DiffRules, new differentiation rules can defined, query whether or not a given rule exists, and symbolically apply rules to simple Julia expressions.

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.

ReferenceTests.jl is a Julia package that adds a couple of additional macros to your testing toolbox. In particular, it focuses on functionality for testing values against reference files, which in turn the package can help create and update if need be.

RecursiveArrayTools.jl is a set of tools for dealing with recursive arrays like arrays of arrays.

This package provides a convenient Julia interface for loading standard named test images and example images for the internal usage in JuliaImages. This can be used in conjunction with the Images package.

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

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.

This package supports lazy analogues of array operations like vcat, hcat, and multiplication. This helps with the implementation of matrix-free methods for iterative solvers.

libwhich is like which, but for dynamic libraries. It is also a bit like ldd and otool -L.