Adaptive 1d numerical Gauss–Kronrod integration in Julia

AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.

Mathematical functions related to statistics.

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A combinatorics library for Julia

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

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

Intel MKL linear algebra backend for Julia

Reduce time-to-first-execution of Julia code

Color manipulation utilities for Julia

Abstract julia interfaces for working with trees

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

Lazy code loading for Julia

Colorschemes, colormaps, gradients, and palettes

Julia package for lazily representing matrices filled with a single entry

＠memoize macro for Julia

Update immutable data

Update deeply nested immutable structs.

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

Julia implementation of the Non-equidistant Fast Fourier Transform (NFFT)

Common functional iterator patterns

Einstein summation notation in Julia

Colored and styled strings for terminals.

Load and create NetCDF files in Julia

Compatibility across Julia versions

Composable Loggers for the Julia Logging StdLib

Multivariate polynomials interface

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

Deprecated. See README. (A Julia package to provide Python-like formatting support)

Julia wrapper for the LLVM C API

The Base interface of the SciML ecosystem

Project Preferences Package

A Julia framework for implementing FFTs

For working with dimensions of arrays by name

Themes for the Julia plotting package Plots.jl

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

Add Conda dependencies to your Julia project