Mathematical functions related to statistics.

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

Convenient input and display of LaTeX equation strings for the Julia language

Color manipulation utilities for Julia

Reduce time-to-first-execution of Julia code

Fortran-like arrays with arbitrary, zero or negative starting indices.

Lazy code loading for Julia

BlockArrays for Julia

Colorschemes, colormaps, gradients, and palettes

Julia package for lazily representing matrices filled with a single entry

Update immutable data

Update deeply nested immutable structs.

An interface to various automatic differentiation backends in Julia.

Julia macro for re-exporting one module from another

Reusable compiler infrastructure for Julia GPU backends.

Simple Traits for Julia

An enhancement to julia piping syntax

Compatibility across Julia versions

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

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

Julia wrapper for the LLVM C API

The Base interface of the SciML ecosystem

Project Preferences Package

A Julia package for representing banded matrices

Themes for the Julia plotting package Plots.jl

Parameterise all the things

Line search methods for optimization and root-finding

Extensions for Julia's docsystem.

Fast parsing machinery for basic types in Julia

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

Uniform Interface for positive definite matrices of various structures

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Functions generated at runtime without world-age issues or overhead

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

Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.