Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

Composable Loggers for the Julia Logging StdLib

Julia interface to ZMQ

It slices, it dices, it splices!

The dispatch doctor prescribes type stability

Multivariate polynomials interface

Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations

Efficient and type-stable physical quantities in Julia

The Base interface of the SciML ecosystem

Julia wrapper for the LLVM C API

Project Preferences Package

Port of mustache.js to julia

A Julia package for representing banded matrices

Parse yer YAMLs

Posix-compliant file name pattern matching

A Julia framework for implementing FFTs

For working with dimensions of arrays by name

Themes for the Julia plotting package Plots.jl

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

Arrays which also have a label for each element for easy scientific machine learning (SciML)

Julia type that implements a drop-in replacement of Array with named dimensions

Algebraic Multigrid in Julia

Parameterise all the things

A Julia implementation of quaternions

Line search methods for optimization and root-finding

Fast parsing machinery for basic types in Julia

Extensions for Julia's docsystem.

Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes

A concrete syntax tree parser for Julia

Tokenization for Julia source code

Observable refs

Uniform Interface for positive definite matrices of various structures

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Package for checking function arguments

Functions generated at runtime without world-age issues or overhead

Interval Sets for Julia

Prime numbers in Julia

Random Number Generators for the Julia Language.

The Arnoldi Method with Krylov-Schur restart, natively in Julia.

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers