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

This package provides another JSON package for Julia, with a focus on speed and slick struct mapping.

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

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 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 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 package introduces the type StructArray which is an AbstractArray whose elements are struct (for example NamedTuples, or ComplexF64, or a custom user defined struct). While a StructArray iterates structs, the layout is column based (meaning each field of the struct is stored in a separate Array).

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

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.

This package contains generic helper algorithms for building plotting components.

This package provides definitions for common functions that are useful for symbolic expression manipulation in Julia. Its purpose is to provide a shared interface between various symbolic programming packages, for example SymbolicUtils.jl, Symbolics.jl, and Metatheory.jl.

This package provides generic methods and modules used in many of the other BioJulia packages. This package defines IO, exceptions, and other types or methods used by other BioJulia packages.

The SortingAlgorithms package provides three sorting algorithms that can be used with Julia's standard sorting API: heapsort, timsort and radixsort.

This package provides tools to express a design pattern for dealing with large/ nested structures, as in machine learning and optimisation. For large machine learning models it can be cumbersome or inefficient to work with parameters as one big, flat vector, and structs help in managing complexity; but it is also desirable to easily operate over all parameters at once, e.g. for changing precision or applying an optimiser update step.

This package takes a string or buffer containing Julia code, performs lexical analysis and returns a stream of tokens.

The goal of RangeArrays is to provide efficient and convenient array data structures where the columns of the arrays are generated (on the fly) by Ranges.

This package only contains and exports a single function realdot(x, y). It computes real(LinearAlgebra.dot(x, y)) while avoiding computing the imaginary part of LinearAlgebra.dot(x, y) if possible. The real dot product is useful when one treats complex numbers as embedded in a real vector space.

ExprTools provides tooling for working with Julia expressions during metaprogramming. This package aims to provide light-weight performant tooling without requiring additional package dependencies.

This package provides the @cse macro, which performs common subexpression elimination.

This package provides a collection of tools for metaprogramming on Julia Expr, the meta programming standard library for MLStyle.

This package provides a method to ensure that arguments to a function conform to a specification.

This package provides a convenient function form of the conditional ifelse. It is similar to Core.ifelse but it is extendable.

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 an interface to line search algorithms implemented in Julia.