This library generalizes and unifies the notion of measures used in Compose, Compose3D, and Escher. It allows building up and representing expressions involving differing types of units that are then evaluated, resolving them into absolute units.

PDMats.jl supports efficient computation on positive definite matrices of various structures. In particular, it provides uniform interfaces to use positive definite matrices of various structures for writing generic algorithms, while ensuring that the most efficient implementation is used in actual computation.

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

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.

This package provides the @OptionalData macro and the corresponding OptData type which is a thin wrapper around a nullable value (of type UnionT, Nothing where T). It allows you to load and access globally available data at runtime in a type-stable way.

Common functional iterator patterns (formerly Iterators.jl).

This package provides an interface to invert functions.

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.

JSON.jl is a pure Julia module which supports parsing and printing JSON documents.

This package defines the BFloat16 data type. The only currently available hardware implementation of this datatype are Google's Cloud TPUs. As such, this package is suitable to evaluate whether using TPUs would cause precision problems for any particular algorithm, even without access to TPU hardware. Note that this package is designed for functionality, not performance, so this package should be used for precision experiments only, not performance experiments.

This package provides Data types and methods for common operations with biological sequences, including DNA, RNA, and amino acid sequences.

Optimisers.jl defines many standard gradient-based optimisation rules, and tools for applying them to deeply nested models.

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

This package provides an interface for readable and writable references to an element of an array or dictionary in Julia.

ItemGraphs is a simple wrapper around LightGraphs that enables most common use case for graph-like data structures: with collection of items that are in relations between each other providing the shortest path between two items.

This package is designed to help in testing ChainRulesCore.frule and ChainRulesCore.rrule methods. The main entry points are ChainRulesTestUtils.frule_test, ChainRulesTestUtils.rrule_test, and ChainRulesTestUtils.test_scalar. Currently this is done via testing the rules against numerical differentiation (using FiniteDifferences.jl).

ChainRulesTestUtils.jl is separated from ChainRulesCore.jl so that it can be a test-only dependency, allowing it to have potentially heavy dependencies, while keeping ChainRulesCore.jl as light-weight as possible.

Static.jl defines a limited set of statically parameterized types and a common interface that is shared between them.

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).

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 manually managed memory buffers backed by NTuples in Julia.

This package provides a Julia wrapper for astronomical library ERFA.

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

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