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 minimalistic package serves as the foundation for working with colors in Julia. It defines basic color types and their constructors, and sets up traits and show methods to make them easier to work with.

This package contains utilities for setting up documentation generation with Documenter.jl.

Implementations of basic math functions which return NaN instead of throwing a DomainError.

This package implements OrderedDicts and OrderedSets, which are similar to containers in base Julia. However, during iteration the Ordered* containers return items in the order in which they were added to the collection.

PositiveFactorizations is a package for computing a positive definite matrix decomposition (factorization) from an arbitrary symmetric input. The motivating application is optimization (Newton or quasi-Newton methods), in which the canonical search direction -H/g (H being the Hessian and g the gradient) may not be a descent direction if H is not positive definite.

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 primitive differentiation rules that can be composed via various formulations of the chain rule. Using DiffRules, new differentiation rules can defined, query whether or not a given rule exists, and symbolically apply rules to simple Julia expressions.

This package provides the DiffResult type, which can be passed to in-place differentiation methods instead of an output buffer.

Julia macros for suppressing and/or capturing output (stdout), warnings (stderr) or both streams at the same time.

This Julia package provides the adapt(T, x) function acts like convert(T, x), but without the restriction of returning a T. This allows you to "convert" wrapper types like Adjoint to be GPU compatible without throwing away the wrapper.

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

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

This package provides a set of tools for working with tabular data in Julia. Its design and functionality are similar to those of Pandas from Python or data.frame, data.table and dplyr from R, making it a great general purpose data science tool, especially for those coming to Julia from R or Python.

RecursiveArrayTools.jl is a set of tools for dealing with recursive arrays like arrays of arrays.

This package implements real and complex polylogarithms, including the real and complex dilogarithm and trilogarithm in Julia.

Measurements.jl is an error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration. The linear error propagation theory is employed to propagate the errors.

A Julia package to contain non-standard matrix factorizations. At the moment it implements the QL, RQ, and UL factorizations, a combined Cholesky factorization with inverse, and polar decompositions. In the future it may include other factorizations such as the LQ factorization.

This package contains a common suite of test functions for stressing the robustness of differentiation tools.

The @unpack and @pack! macros work to unpack types, modules, and dictionaries.

This library provides tools for working with Julia code and expressions. This includes a template-matching system and code-walking tools that let you do deep transformations of code.

OffsetArrays.jl provides Julia users with arrays that have arbitrary indices, similar to those found in some other programming languages like Fortran.

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.