Gumbo.jl is a Julia wrapper around Google's gumbo library for parsing HTML.

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.

Quaternions are best known for their suitability as representations of 3D rotational orientation. They can also be viewed as an extension of complex numbers.

Minimal package which enables to add custom gradients to Zygote, without depending on Zygote itself.

This is a Julia package that defines an IniFile type that interfaces with .ini files.

AstroTime.jl provides a high-precision, time-scale aware, DateTime-like data type which supports all commonly used astronomical time scales.

Crayons is a package that makes it simple to write strings in different colors and styles to terminals. It supports the 16 system colors, both the 256 color and 24 bit true color extensions, and the different text styles available to terminals.

This package just exports one type: the InvertedIndex, or Not for short. It can wrap any supported index type and may be used as an index into any AbstractArray subtype, including OffsetArrays.

StatsBase.jl is a Julia package that provides basic support for statistics. Particularly, it implements a variety of statistics-related functions, such as scalar statistics, high-order moment computation, counting, ranking, covariances, sampling, and empirical density estimation.

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.

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 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.

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.

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 a minimal String type for Julia that allows for efficient string representation and transfer

This package provides a type stable and efficient wrapper of arbitrary functions.

Optimisers.jl defines many standard gradient-based optimisation rules, and tools for applying them to deeply nested models.

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.

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

Jive.jl is a Julia package to help with writing tests.

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.

A Julia package for evaluating distances(metrics) between vectors. This package also provides optimized functions to compute column-wise and pairwise distances, which are often substantially faster than a straightforward loop implementation.

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 various examples.