In-memory tabular data in Julia

CUDA programming in Julia.

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

Formatted output of timed sections in Julia

A benchmarking framework for the Julia language

Basic statistics for Julia

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

Print data in formatted tables.

A data structure for mathematical optimization problems

Heterogeneous programming in Julia

Special mathematical functions in Julia

Reusable array functionality for Julia's various GPU backends.

JSON parsing and printing

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

An interface for tables in Julia

Adaptive 1d numerical Gauss–Kronrod integration in Julia

A Julia interface to the NLopt nonlinear-optimization library

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

Mathematical functions related to statistics.

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

Lazy code loading for Julia

Julia package for lazily representing matrices filled with a single entry

Julia macro for re-exporting one module from another

Reusable compiler infrastructure for Julia GPU backends.

Colored and styled strings for terminals.

Compatibility across Julia versions

Julia wrapper for the LLVM C API

Project Preferences Package

A Julia framework for implementing FFTs

Arrays for working with categorical data (both nominal and ordinal)

Line search methods for optimization and root-finding