Powerful convenience for Julia visualizations and data analysis

In-memory tabular data in Julia

21st century AD

Optimization functions for Julia

A Julia package for probability distributions and associated functions.

Forward Mode Automatic Differentiation for Julia

Statically sized arrays for Julia

Progress meter for long-running computations

Julia implementation of Data structures

HTTP for Julia

A benchmarking framework for the Julia language

Physical quantities with arbitrary units

Basic statistics for Julia

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

HDF5-compatible file format in pure Julia

Fast, continuous interpolation of discrete datasets in Julia

Utility library for working with CSV and other delimited files in the Julia programming language

Forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

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

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

Print data in formatted tables.

Query almost anything in julia

A data structure for mathematical optimization problems

Filter design, periodograms, window functions, and other digital signal processing functionality

A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)

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

Special mathematical functions in Julia

Probabilistic Programming with Gaussian processes in Julia

Efficient implementation of struct arrays in Julia

Reusable array functionality for Julia's various GPU backends.

Simple curve fitting in Julia

JSON parsing and printing

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

Polynomial manipulations in Julia

An interface for tables in Julia

High accuracy derivatives, estimated via numerical finite differences (formerly FDM.jl)

Julia bindings to the FFTW library for fast Fourier transforms

Adaptive 1d numerical Gauss–Kronrod integration in Julia

Julia package for kernel functions for machine learning

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.