This package calculates approximate derivatives numerically using finite difference.

This package provides a documentation generator for Julia.

This package provides a Julia interface defining a collection of types (without instances) for implementing conventions about the scientific interpretation of data. This package makes a distinction between the machine type and the scientific type of a Julia object. A machine type refers to the Julia type being used to represent the object, for instance Float64. The scientific type refers to how the object should be interpreted, for instance Continuous or Multiclass3.

ANSIColoredPrinters.jl converts a text qualified by ANSI escape codes to another format.

This package provides a simple and flexible IR format, expressive enough to work with both lowered and typed Julia code, as well as external IRs. It can be used with Julia metaprogramming tools such as Cassette.

This package lazily represents matrices filled with a single entry, as well as identity matrices. This package exports the following types: Eye, Fill, Ones, Zeros, Trues and Falses.

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

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.

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 provides reader/writer for delimited text data, as comma-delimited (csv), tab-delimited (tsv), or otherwise.

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.

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 various examples.

This package provides these irrational constants:

• twoπ = 2π

• fourπ = 4π

• halfπ = π / 2

• quartπ = π / 4

• invπ = 1 / π

• twoinvπ = 2 / π

• fourinvπ = 4 / π

• inv2π = 1 / (2π)

• inv4π = 1 / (4π)

• sqrt2 = √2

• sqrt3 = √3

• sqrtπ = √π

• sqrt2π = √2π

• sqrt4π = √4π

• sqrthalfπ = √(π / 2)

• invsqrt2 = 1 / √2

• invsqrtπ = 1 / √π

• invsqrt2π = 1 / √2π

• loghalf = log(1 / 2)

• logtwo = log(2)

• logten = log(10)

• logπ = log(π)

• log2π = log(2π)

• log4π = log(4π)

This package provides a wide array of functions for dealing with color. This includes conversion between colorspaces, measuring distance between colors, simulating color blindness, parsing colors, and generating color scales for graphics.

This package provides manually managed memory buffers backed by NTuples in Julia.

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

Graphics.jl is an abstraction layer for graphical operations in Julia.

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

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.

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

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.

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.