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

This package is an add-on to ColorTypes.jl and provides fast mathematical operations for objects with types such as RGB and Gray. Specifically, with this package both grayscale and RGB colors are treated as if they are points in a normed vector space.

This package compiles regular expressions into Julia code, which is then compiled into low-level machine code by the Julia compiler. The package is designed to generate very efficient code to scan large text data, which is often much faster than handcrafted code. Automa.jl can insert arbitrary Julia code that will be executed in state transitions. This makes it possible, for example, to extract substrings that match a part of a regular expression.

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

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.

The Tables.jl package provides simple, yet powerful interface functions for working with all kinds tabular data.

This package parses YAML documents into native Julia types and dumps them back into YAML documents.

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.

ArnoldiMethod.jl provides an iterative method to find a few approximate solutions to the eigenvalue problem in standard form with main goals:

• Having a native Julia implementation of the eigs function that performs as well as ARPACK. With native we mean that its implementation should be generic and support any number type. Currently the partialschur function does not depend on LAPACK, and removing the last remnants of direct calls to BLAS is in the pipeline.

• Removing the dependency of the Julia language on ARPACK. This goal was already achieved before the package was stable enough, since ARPACK moved to a separate repository Arpack.jl.

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

This package provides a simple Julian API to use the libsass library to compile scss and sass files to css.

This package provides zlib codecs for TranscodingStreams.jl.

This package provides a set of custom string types of various fixed sizes. Each inline string is a custom primitive type and can benefit from being stack friendly by avoiding allocations/heap tracking in the GC. When used in an array, the elements are able to be stored inline since each one has a fixed size. Currently support inline strings from 1 byte up to 255 bytes.

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

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

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

This minimalistic package serves as the foundation for other SIMD packages in Julia.

This package calculates approximate derivatives numerically using finite difference.

Parsers.jl is a collection of type parsers and utilities for Julia.

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.

An IndirectArray is one that encodes data using a combination of an index and a value table. Each element is assigned its own index, which is used to retrieve the value from the value table. Among other uses, IndirectArrays can represent indexed images, sometimes called "colormap images" or "paletted images."

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

This package offers Python-style general formatting and c-style numerical formatting.

This package aims at establishing common ground for Optim.jl, LineSearches.jl, and NLsolve.jl. The common ground is mainly the types used to hold objective related callables, information about the objectives, and an interface to interact with these types.