This package is intended as a lightweight foundation for tensor operations across the Julia ecosystem. Currently it exports three operations: hadamard, tensor, and boxdot.

This package provides representations for infinity and negative infinity in Julia.

This package provides Data types and methods for common operations with biological sequences, including DNA, RNA, and amino acid sequences.

The Schur decomposition is the workhorse for eigensystem analysis of dense matrices. The diagonal eigen-decomposition of normal (especially Hermitian) matrices is an important special case, but for non-normal matrices the Schur form is often more useful.

This package provides a functionality of files download with cURL, wget or HTTP.jl backends.

This package provides tools for working with the basic calculus operations of differentiation and integration. The Calculus package produces approximate derivatives by several forms of finite differencing or produces exact derivative using symbolic differentiation. It can also be used to compute definite integrals by different numerical methods.

This package allows programmers to explicitly SIMD-vectorize their Julia code. By exposing SIMD vector types and corresponding operations, the programmer can explicitly vectorize their code. While this does not guarantee that the generated machine code is efficient, it relieves the compiler from determining whether it is legal to vectorize the code, deciding whether it is beneficial to do so, and rearranging the code to synthesize vector instructions.

This package provides a wrapper around ImageMagick version 6. It was split off from Images.jl to make image I/O more modular.

This package make loading packages faster, maybe. It supports specifying glue code in packages which will load automatically when another package is loaded, so that explicit dependencies (and long load times) can be avoided.

FixedPointNumbers.jl implements fixed-point number types for Julia. A fixed-point number represents a fractional, or non-integral, number. In contrast with the more widely known floating-point numbers, with fixed-point numbers the decimal point doesn't "float": fixed-point numbers are effectively integers that are interpreted as being scaled by a constant factor. Consequently, they have a fixed number of digits (bits) after the decimal (radix) point.

This package provides the ability to directly call and fully interoperate with Python from the Julia language. You can import arbitrary Python modules from Julia, call Python functions (with automatic conversion of types between Julia and Python), define Python classes from Julia methods, and share large data structures between Julia and Python without copying them.

This package implements handy macros @recipe and @series which will define a custom transformation and attach attributes for user types. Its design is an attempt to simplify and generalize the summary and display of types and data from external packages. With this package it is possible to describe visualization routines that can be used as components in more complex visualizations.

Optim.jl is a package for univariate and multivariate optimization of functions.

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 combinatorics library for Julia, focusing mostly (as of now) on enumerative combinatorics and permutations.

LightGraphs offers both (a) a set of simple, concrete graph implementations -- Graph (for undirected graphs) and DiGraph (for directed graphs), and (b) an API for the development of more sophisticated graph implementations under the AbstractGraph type.

BenchmarkTools.jl makes performance tracking of Julia code easy by supplying a framework for writing and running groups of benchmarks as well as comparing benchmark results.

This package provides a namespace for data-related generic function definitions to solve the optional dependency problem; packages wishing to share and/or extend functions can avoid depending directly on each other by moving the function definition to DataAPI.jl and each package taking a dependency on it.

This package provides support for image resizing, image rotation, and other spatial transformations of arrays.

BSON.jl is a Julia package for working with the Binary JSON serialisation format. It can be used as a general store for Julia data structures.

JSON.jl is a pure Julia module which supports parsing and printing JSON documents.

This package provides definitions for most of the primary types and functions in StaticArrays.jl. This enables downstream packages to implement new methods on these types without depending on the entirety of StaticArrays.jl.

This package defines the Bijection data type. A Bijection data structure behaves similar to a Dict, however it prevents assigning the same value to two different keys.

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.