StatsBase.jl is a Julia package that provides basic support for statistics. Particularly, it implements a variety of statistics-related functions, such as scalar statistics, high-order moment computation, counting, ranking, covariances, sampling, and empirical density estimation.

This package provides a display system which enables the user handle multiple input/output devices and decide what media types get displayed where.

The is package provides a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives.

This package provides information about the features of the host CPU in Julia.

This is a Julia interface to libquadmath, providing a Float128 type corresponding to the IEEE754 binary128 floating point format.

The purpose of this package is partly to extend linear algebra functionality in base to cover generic element types, e.g. BigFloat and Quaternion, and partly to be a place to experiment with fast linear algebra routines written in Julia (except for optimized BLAS).

PlotThemes is a package to spice up the plots made with Plots.jl.

This package contains the underlying query operators that are exposed to users in Query.jl.

This package provides a type stable and efficient wrapper of arbitrary functions.

This package implements a trait-based framework for describing array layouts such as column major, row major, etc. that can be dispatched to appropriate BLAS or optimised Julia linear algebra routines. This supports a much wider class of matrix types than Julia's in-built StridedArray.

Quaternions are best known for their suitability as representations of 3D rotational orientation. They can also be viewed as an extension of complex numbers.

TableTraits defines a generic interface for tabular data.

This package provides a Julia interface to the Matplotlib plotting library from Python, and specifically to the matplotlib.pyplot module. PyPlot uses the Julia PyCall package to call Matplotlib directly from Julia with little or no overhead (arrays are passed without making a copy).

This library generalizes and unifies the notion of measures used in Compose, Compose3D, and Escher. It allows building up and representing expressions involving differing types of units that are then evaluated, resolving them into absolute units.

This package provides a C-compatible enum for Julia.

A Julia package for evaluating distances(metrics) between vectors. This package also provides optimized functions to compute column-wise and pairwise distances, which are often substantially faster than a straightforward loop implementation.

This small package supports the representation of images as AxisArrays to endow the axes with "meaning," and makes programming with such arrays easy via traits.

This package implements "lazy" in-place elementwise transformations of arrays for the Julia programming language. Explicitly, it provides a "view" M of an array A so that M[i] = f(A[i]) for a specified (but arbitrary) function f, without ever having to compute M explicitly (in the sense of allocating storage for M). The name of the package comes from the fact that M == map(f, A).

The goal of RangeArrays is to provide efficient and convenient array data structures where the columns of the arrays are generated (on the fly) by Ranges.

This package provides support for decoding and encoding texts between multiple character encodings. It is currently based on the iconv interface, and supports all major platforms using GNU libiconv.

This package is designed to help in testing ChainRulesCore.frule and ChainRulesCore.rrule methods. The main entry points are ChainRulesTestUtils.frule_test, ChainRulesTestUtils.rrule_test, and ChainRulesTestUtils.test_scalar. Currently this is done via testing the rules against numerical differentiation (using FiniteDifferences.jl).

ChainRulesTestUtils.jl is separated from ChainRulesCore.jl so that it can be a test-only dependency, allowing it to have potentially heavy dependencies, while keeping ChainRulesCore.jl as light-weight as possible.

This package provides support for the Woodbury matrix identity for the Julia programming language. This is a generalization of the Sherman-Morrison formula. Note that the Woodbury matrix identity is notorious for floating-point roundoff errors, so be prepared for a certain amount of inaccuracy in the result.

This package enables the Markdown / MkDocs backend of Documenter.jl.

The purpose of this package is to allow Julia function calls to be temporarily overloaded for the purpose of testing.