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

CoordinateTransformations is a Julia package to manage simple or complex networks of coordinate system transformations. Transformations can be easily applied, inverted, composed, and differentiated (both with respect to the input coordinates and with respect to transformation parameters such as rotation angle). Transformations are designed to be light-weight and efficient enough for, e.g., real-time graphical applications, while support for both explicit and automatic differentiation makes it easy to perform optimization and therefore ideal for computer vision applications such as SLAM (simultaneous localization and mapping).

This package has the purpose to print data in matrices in a human-readable format.

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

Quaternions are best known for their suitability as representations of 3D rotational orientation. They can also be viewed as an extension of complex numbers.

This package defines functions for getting multiple indices out of dictionaries, tuples, etc, extending this ability beyond AbstractArray.

This package contains types with default field values, keyword constructors and (un-)pack macros. Keyword functions can be slow in Julia, however, the normal positional constructor is also provided and could be used in performance critical code.

This package provides fall-back implementations for a collection of traits possessed by statistical objects. A trait is a function with a single arguments that is a Julia type, which might encode type metadata for inspection or for use in function dispatch.

This package provides a collection of colorschemes.

This package defines a new operator for composition of morphisms.

This package provides support for one-dimensional numerical integration in Julia using adaptive Gauss-Kronrod quadrature. The code was originally part of Base Julia. It supports integration of arbitrary numeric types, including arbitrary precision (BigFloat), and even integration of arbitrary normed vector spaces (e.g. matrix-valued integrands).

This package just exports one type: the InvertedIndex, or Not for short. It can wrap any supported index type and may be used as an index into any AbstractArray subtype, including OffsetArrays.

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 is the counterpart of AbstractArray interface, but for GPU array types. It provides functionality and tooling to speed-up development of new GPU array types. This package is not intended for end users; instead, you should use one of the packages that builds on GPUArrays.jl, such as CUDA.jl, oneAPI.jl or AMDGPU.jl.

This package provides Julia implementation for reading and writing FITS files, based on the cfitsio library.

This package contains the testset from Julia, packaged into a loadable module.

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.

This package provides definitions for most of the primary types and functions in StaticArrays.jl. This enables downstream packages to implement new methods on these types without depending on the entirety of StaticArrays.jl.

This package provides a fast, extensible progress bar for Julia. This can help users track the progress of long-running tasks.

This package provides a collection of useful extensions for Julia's built-in docsystem. These are features that are not yet mature enough to be considered for inclusion in Base, or that have sufficiently niche use cases that including them with the default Julia installation is not seen as valuable enough at this time.

BenchmarkTools.jl makes performance tracking of Julia code easy by supplying a framework for writing and running groups of benchmarks as well as comparing benchmark results.

This package provides a set of tools for working with tabular data in Julia. Its design and functionality are similar to those of Pandas from Python or data.frame, data.table and dplyr from R, making it a great general purpose data science tool, especially for those coming to Julia from R or Python.

This package exports following operations over bit vectors with extremely fast speed while keeping extra memory usage small:

• getindex(bv::IndexableBitVectors, i::Integer): i-th element of bv

• rank(b::Bool, bv::AbstractIndexableBitVector, i::Integer): the number of occurrences of bit b in bv[1:i]

• select(b::Bool, bv::AbstractIndexableBitVector, i::Integer): the index of i-th occurrence of b in bv.

and other shortcuts or types.

This package implements various 3D rotation parameterizations and defines conversions between them. At their heart, each rotation parameterization is a 3×3 unitary (orthogonal) matrix (based on the StaticArrays.jl package), and acts to rotate a 3-vector about the origin through matrix-vector multiplication.