Powerful convenience for Julia visualizations and data analysis

Package to call Python functions from the Julia language

Automatically update function definitions in a running Julia session

A Julia package for probability distributions and associated functions.

Optimization functions for Julia

Forward Mode Automatic Differentiation for Julia

Macro(s) for vectorizing loops.

Statically sized arrays for Julia

Julia implementation of Data structures

Progress meter for long-running computations

HTTP for Julia

An experimental code analyzer for Julia. No need for additional type annotations.

Basic statistics for Julia

Convert julia objects to LaTeX equations, arrays or other environments.

The slow descent into madness

Plotting for Julia based on matplotlib.pyplot

Fast, continuous interpolation of discrete datasets in Julia

HDF5-compatible file format in pure Julia

Types with default field values, keyword constructors and (un-)pack macros

A Julia package for evaluating distances (metrics) between vectors.

An optimized graphs package for the Julia programming language

High performance nearest neighbor data structures and algorithms for Julia.

Plotting for Julia based on GR, a framework for visualisation applications

A Julia package for data clustering

Special mathematical functions in Julia

JSON parsing and printing

Making packages work faster with more extensive precompilation

A Julia package for defining and working with linear maps, also known as linear transformations or linear operators acting on vectors. The only requirement for a LinearMap is that it can act on a vector (by multiplication) efficiently.

MacroTools provides a library of tools for working with Julia code and expressions.

Reusable array functionality for Julia's various GPU backends.

An interface for tables in Julia

Julia package for Gaussian quadrature

Root finding functions for Julia

Calculus functions in Julia

Mathematical functions related to statistics.

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.

A Julia frontend, written in Julia

Julia bindings to the FFTW library for fast Fourier transforms

Adaptive 1d numerical Gauss–Kronrod integration in Julia

Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support