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SparseArrays.jl is a Julia stdlib

This is what I wish `Base.@enum` was.

Simple, consistent interfaces for any codec.

Cunning tricks though the julia compiler internals

Collective tools for metaprogramming on Julia Expr

`@pack!` and `@unpack` macros

A simple index type that allows for inverted selections

Filesystem path types in julia

Fixed point types for julia

TAR files: create, list, extract them in pure Julia

Light-weight expression manipulation tools

Basic color definitions and traits

🏩🏠🌆🏨🌇🏦

A simple shared suite of common derivative definitions

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Julia package for various special functions based on `log` and `exp`.

Missing value support for Julia

Julia Wrappers for the arpack-ng Fortran library

A Julia package for calculating hypergeometric functions

Base library providing vectorization-tools (ie, SIMD) that other libraries are built off of.

Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.

Scratch spaces for all your persistent mutable data needs

A Julia package for representing block-banded matrices and banded-block-banded matrices

Ask the CPU for cache sizes, SIMD feature support, a running hypervisor, and more.

A Julia package for describing array layouts and more general fast linear algebra

Faster closure variable capture

Extra sorting algorithms extending Julia's sorting API

Julia math built-ins which return NaN and accumulator functions which ignore NaN

Static types useful for dispatch and generated functions.

Makes Julia's package extensions backwards compatible

Zlib codecs for TranscodingStreams.jl.

Interface for arithmetics on mutable types in Julia

A pooled representation for arrays with few unique elements

Fixed-width string types for Julia

A performant, 100% native-julia SHA1, SHA2, and SHA3 implementation

Calculating contour curves for 2D scalar fields in Julia

This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem