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

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

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.

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.

MbedTLS.jl provides a wrapper around the mbed TLS and cryptography C library for Julia.

This package provides several functions to manipulate strings with ANSI escape sequences.

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.

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

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

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 is intended to implement a "minimal" foundation for intervals upon which other packages might build. In particular, we encourage type-piracy for the reason that only one interval package can unambiguously define the .. and ± operators.

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 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 provides a C-compatible enum for Julia.

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.

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.

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.

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

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.

This package provides a function extrapolate that extrapolates a given function f(x) to f(x0), evaluating f only at a geometric sequence of points > x0 (or optionally < x0). The key algorithm is Richardson extrapolation using a Neville–Aitken tableau, which adaptively increases the degree of an extrapolation polynomial until convergence is achieved to a desired tolerance (or convergence stalls due to e.g. floating-point errors). This allows one to obtain f(x0) to high-order accuracy, assuming that f(x0+h) has a Taylor series or some other power series in h.

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.

Extents.jl is a small package that defines an Extent object that can be used by the different Julia spatial data packages. Extent is a wrapper for a NamedTuple of tuples holding the lower and upper bounds for each dimension of a object.

ReverseDiff.jl is a fast and compile-able tape-based reverse mode AD, that implements methods to take gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really).

This package provides a collection of colorschemes.