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 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.

The goal of RangeArrays is to provide efficient and convenient array data structures where the columns of the arrays are generated (on the fly) by Ranges.

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

This package implements real and complex polylogarithms, including the real and complex dilogarithm and trilogarithm in Julia.

This package provides tools to re-export modules and symbols.

This very lightweight package provides primitive functions for construction of objects.

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 provides zlib codecs for TranscodingStreams.jl.

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

This package provides bit twiddling convenience functions in Julia. These are useful for going to the next or previous mask size or for calculating corresponding shifts.

This package defines the Bijection data type. A Bijection data structure behaves similar to a Dict, however it prevents assigning the same value to two different keys.

This package provides a method to ensure that arguments to a function conform to a specification.

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

This package implements a trait-based framework for describing array layouts such as column major, row major, etc. that can be dispatched to appropriate BLAS or optimised Julia linear algebra routines. This supports a much wider class of matrix types than Julia's in-built StridedArray.

Graphics.jl is an abstraction layer for graphical operations in Julia.

This package provides support for decoding and encoding texts between multiple character encodings. It is currently based on the iconv interface, and supports all major platforms using GNU libiconv.

When visualizing images, it is not uncommon to provide a 2D view of different image sources. For example, comparing multiple images of different sizes, getting a preview of machine learning dataset. This package aims to provide easy-to-use tools for such tasks.

This package provides a collection of colorschemes.

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.

CommonSolve.jl provides solve, init, solve!, and step! commands. By using the same definition, solver libraries from other completely different ecosystems can extend the functions and thus not clash with SciML if both ecosystems export the solve command.

This module provides a Julia interface to GR, a framework for visualisation applications.

SoftGlobalScope is a package for the Julia language that simplifies the variable scoping rules for code in global scope. It is intended for interactive shells to make it easier to work interactively with Julia, especially for beginners.

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.