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This package provides types similar to Julia's Rational type, which make some sacrifices but have better computational performance.
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 alignment algorithms and data structures for sequence of DNA, RNA, and amino acid sequences.
This package provides Julia implementation of C-style interface to CFITSIO functions with following features:
Function names closely mirror the C interface (e.g.,
fits_open_file()).Functions operate on
FITSFile, a thin wrapper for fitsfile C struct (FITSFilehas concept of "current HDU", as in CFITSIO).Wrapper functions do check the return status from CFITSIO and throw an error with the appropriate message.
FuzzyCompletions provides fuzzy completions for a Julia runtime session.
This package make loading packages faster, maybe. It supports specifying glue code in packages which will load automatically when another package is loaded, so that explicit dependencies (and long load times) can be avoided.
This package is intended as a lightweight foundation for tensor operations across the Julia ecosystem. Currently it exports three operations: hadamard, tensor, and boxdot.
This module provides support for reading and writing ZIP archives in Julia.
MsgPack.jl is a MessagePack implementation in pure Julia, with type-driven, overloadable packing/unpacking functionality.
Millboard.jl provides a library for getting data in a tablized format to arrange into rows and columns of cells.
This package supports lazy analogues of array operations like vcat, hcat, and multiplication. This helps with the implementation of matrix-free methods for iterative solvers.
This package provides an implementation of shifted arrays for Julia.
Showoff provides an interface for consistently formatting an array of n things, e.g. numbers, dates, unitful values. It's used in Gadfly, Plots and Makie to label axes and keys.
This package provides the StructTypes.StructType trait for Julia types to declare the kind of "struct" they are, providing serialization/deserialization packages patterns and strategies to automatically construct objects.
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 DataAPI.jl and each package taking a dependency on it.
This package provides a lightweight string parsing and representation of angles.
This package provides primitive differentiation rules that can be composed via various formulations of the chain rule. Using DiffRules, new differentiation rules can defined, query whether or not a given rule exists, and symbolically apply rules to simple Julia expressions.
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 implements a variety of data structures, including, CircularBuffer, Queue, Stack, Accumulators, LinkedLists, SortedDicts and many others.
This package provides an implementation of an associative container mapping (K,V) pairs via the type IntervalTreeK, V. The type K may be any ordered type.
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.
This package provides a summary of available CPU features in Julia.
Optim.jl is a package for univariate and multivariate optimization of functions.
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.