The DualNumbers Julia package defines the Dual type to represent dual numbers, and supports standard mathematical operations on them. Conversions and promotions are defined to allow performing operations on combinations of dual numbers with predefined Julia numeric types.

This package provides a collection of useful bit-twiddling tricks, ready to use as functions, with detailed documentation and example real-world use cases.

This package provides an alternative interface for dictionaries in Julia, for improved productivity and performance.

This package calculates approximate derivatives numerically using finite difference.

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 defines functionality to calculate volume element changes for functions that perform a change of variables (like coordinate transformations).

The purpose of this package is to provide test problems for JuliaNLSolvers packages.

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.

Crayons is a package that makes it simple to write strings in different colors and styles to terminals. It supports the 16 system colors, both the 256 color and 24 bit true color extensions, and the different text styles available to terminals.

TypedTables.jl provides two column-based storage containers: Table and FlexTable, both of which represent an array of NamedTuples. This package is designed to be lightweight, easy-to-use and fast, and presents a very minimal new interface to learn.

This package provides tools to help you develop code. Juno is built on the Atom text editor. Juno consists of both Julia and Atom packages in order to add Julia-specific enhancements, such as syntax highlighting, a plot pane, integration with Julia's debugger, a console for running code, and much more.

Consider that the package is “maintenance-only mode” and only receives bug fixes. The Julia IDE effort is pointed to extension for VSCode.

LeapSeconds provides a functionality to return the difference between TAI and UTC or vice versa for a given date. For dates after 1972-01-01, this is the number of leap seconds.

This package provides a minimal String type for Julia that allows for efficient string representation and transfer

This package implements methods to take derivatives, gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really) using forward mode automatic differentiation (AD).

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 lazily represents matrices filled with a single entry, as well as identity matrices. This package exports the following types: Eye, Fill, Ones, Zeros, Trues and Falses.

CommonSolve.jl provides solve, init, solve!, and step! commands. By using the same definition, solver libraries from other completely different ecosystems can extend the functions and thus not clash with SciML if both ecosystems export the solve command.

This package provides tools to re-export modules and symbols.

This package provides manually managed memory buffers backed by NTuples in Julia.

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.

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.

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.

DeepDiffs.jl provides the deepdiff function, which finds and displays differences (diffs) between Julia data structures. It supports Vectors, Dicts, and Strings. When diffing dictionaries where values associated with a particular key may change, deepdiff will recurse into value to provide a more detailed diff.

TableTraits defines a generic interface for tabular data.