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.

Polynomial manipulations in Julia

An interface for tables in Julia

Hypothesis tests for Julia

XGBoost Julia Package

Middleware for Julia

Excel file reader and writer for the Julia language.

Adaptive 1d numerical Gauss–Kronrod integration in Julia

Julia bindings to Vega-Lite

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.

Main Package for IO, loading all different kind of files

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A combinatorics library for Julia

Julia package registration bot

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

Neural Network primitives with multiple backends

Abstract julia interfaces for working with trees

Lazy code loading for Julia

Colorschemes, colormaps, gradients, and palettes

Julia package for lazily representing matrices filled with a single entry

Update immutable data

Conda managing Julia binary dependencies

A Julia package for interfacing with GitHub

Update deeply nested immutable structs.

Julia macro for re-exporting one module from another

Core functionality for the MLJ machine learning framework

Reusable compiler infrastructure for Julia GPU backends.

Simple Traits for Julia

An enhancement to julia piping syntax

A meta package for data science in julia

Colored and styled strings for terminals.

Math with more good bits

Generic numerical linear algebra in Julia

Julia package of loss functions for machine learning.

Compatibility across Julia versions