Powerful convenience for Julia visualizations and data analysis

In-memory tabular data in Julia

Package to call Python functions from the Julia language

CUDA programming in Julia.

A Julia package for probability distributions and associated functions.

Forward Mode Automatic Differentiation for Julia

Statically sized arrays for Julia

Julia implementation of Data structures

Formatted output of timed sections in Julia

HTTP for Julia

Physical quantities with arbitrary units

Generalized linear models in Julia

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

An optimized graphs package for the Julia programming language

Statistical plotting recipes for Plots.jl

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

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

High performance nearest neighbor data structures (KDTree and BallTree) and algorithms for Julia.

Print data in formatted tables.

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

Heterogeneous programming in Julia

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

A Julia package for data clustering

Special mathematical functions in Julia

Root finding functions for 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.

An interface for tables in Julia

Hypothesis tests for Julia

Julia bindings to the FFTW library for fast Fourier transforms

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.

Specifying, fitting, and evaluating statistical models in Julia

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