PaddedViews provides a simple wrapper type, PaddedView, to add "virtual" padding to any array without copying data. Edge values not specified by the array are assigned a fillvalue. Multiple arrays may be "promoted" to have common indices using the paddedviews function.

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.

This is a Julia package that defines an IniFile type that interfaces with .ini files.

This package supports SI units and also many other unit system.

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

FilePathsBase.jl provides a type based approach to working with filesystem paths in Julia.

This package was factored out of Plots.jl to allow any other plotting package to use the recipe pipeline. In short, the extremely lightweight RecipesBase.jl package can be depended on by any package to define "recipes": plot specifications of user-defined types, as well as custom plot types. RecipePipeline.jl contains the machinery to translate these recipes to full specifications for a plot.

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

This package provides types similar to Julia's Rational type, which make some sacrifices but have better computational performance.

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.

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.

A block array is a partition of an array into blocks or subarrays. This package has two purposes. Firstly, it defines an interface for an AbstractBlockArray block arrays that can be shared among types representing different types of block arrays. The advantage to this is that it provides a consistent API for block arrays. Secondly, it also implements two different type of block arrays that follow the AbstractBlockArray interface. The type BlockArray stores each block contiguously while the type PseudoBlockArray stores the full matrix contiguously. This means that BlockArray supports fast non copying extraction and insertion of blocks while PseudoBlockArray supports fast access to the full matrix to use in in for example a linear solver.

This package provides consistent and extensible functional programming infrastructures, and metaprogramming facilities.

Minimal package which enables to add custom gradients to Zygote, without depending on Zygote itself.

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

This package provides additional functionality for working with missing values in Julia.

ImageCore is the lowest-level component of the system of packages designed to support image processing and computer vision.

Tracker.jl previously provided Flux.jl with automatic differentiation for its machine learning platform.

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.

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

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 finds the first occurrence of a byte or set of bytes in a chunk of memory. Think of it like a much faster version of findfirst that only iterates over bytes in memory.

FileIO aims to provide a common framework for detecting file formats and dispatching to appropriate readers/writers. The two core functions in this package are called load and save, and offer high-level support for formatted files (in contrast with Julia's low-level read and write).

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 StatsAPI.jl and each package taking a dependency on it.