Open Source Julia Business Software for Linux
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Julia Business Software for Linux
View 128 business solutionsBrowse free open source Julia Business Software for Linux and projects below. Use the toggles on the left to filter open source Julia Business Software for Linux by OS, license, language, programming language, and project status.
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MATLAB
Calling MATLAB in Julia through MATLAB Engine
The MATLAB.jl package provides an interface for using MATLAB® from Julia using the MATLAB C api. In other words, this package allows users to call MATLAB functions within Julia, thus making it easy to interoperate with MATLAB from the Julia language. You cannot use MATLAB.jl without having purchased and installed a copy of MATLAB® from MathWorks. This package is available free of charge and in no way replaces or alters any functionality of MathWorks's MATLAB product. -
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Finch.jl
Sparse tensors in Julia and more
Finch is a cutting-edge Julia-to-Julia compiler specially designed for optimizing loop nests over sparse or structured multidimensional arrays. Finch empowers users to write conventional for loops which are transformed behind-the-scenes into fast sparse code. -
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CxxWrap
Package to make C++ libraries available in Julia
This package aims to provide a Boost. Python-like wrapping for C++ types and functions to Julia. The idea is to write the code for the Julia wrapper in C++, and then use a one-liner on the Julia side to make the wrapped C++ library available there. The mechanism behind this package is that functions and types are registered in C++ code that is compiled into a dynamic library. This dynamic library is then loaded into Julia, where the Julia part of this package uses the data provided through a C interface to generate functions accessible from Julia. The functions are passed to Julia either as raw function pointers (for regular C++ functions that don't need argument or return type conversion) or std::functions (for lambda expressions and automatic conversion of arguments and return types). The Julia side of this package wraps all this into Julia methods automatically. -
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DFTK.jl
Density-functional toolkit
The density-functional toolkit, DFTK for short, is a collection of Julia routines for experimentation with plane-wave density-functional theory (DFT). The unique feature of this code is its emphasis on simplicity and flexibility with the goal of facilitating algorithmic and numerical developments as well as interdisciplinary collaboration in solid-state research. -
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JET.jl
An experimental code analyzer for Julia
JET employs Julia's type inference system to detect potential bugs and type instabilities. JET is tightly coupled to the Julia compiler, and so each JET release supports a limited range of Julia versions. See the Project.toml file for the range of supported Julia versions. The Julia package manager should install a version of JET compatible with the Julia version you are running. If you want to use JET on unreleased version of Julia where compatibility with JET is yet unknown, clone this git repository and dev it, such that Julia compatibility is ignored. -
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Makie
Interactive data visualizations and plotting in Julia
Makie is an interactive data visualization and plotting ecosystem for the Julia programming language, available on Windows, Linux, and Mac. The backend packages GLMakie, WGLMakie, CairoMakie and RPRMakie add different functionalities: You can use Makie to interactively explore your data and create simple GUIs in native Windows or web browsers, export high-quality vector graphics or even raytrace with physically accurate lighting. Choose one or more backend packages: GLMakie (interactive OpenGL in native OS windows), WGLMakie (interactive WebGL in browsers, IDEs, notebooks), CairoMakie (static 2D vector graphics and images), and RPRMakie (raytracing). Each backend re-exports all of Makie.jl so you don't have to install or load it explicitly. -
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ModelingToolkitStandardLibrary.jl
A standard library of components to model the world and beyond
The ModelingToolkit Standard Library is a standard library of components to model the world and beyond. -
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NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers
Fast implementations of root-finding algorithms in Julia that satisfy the SciML common interface. For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation that contains the unreleased features. NonlinearSolve.jl is a unified interface for the nonlinear solving packages of Julia. The package includes its own high-performance nonlinear solvers which include the ability to swap out to fast direct and iterative linear solvers, along with the ability to use sparse automatic differentiation for Jacobian construction and Jacobian-vector products. NonlinearSolve.jl interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code. -
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Enzyme.jl
Julia bindings for the Enzyme automatic differentiator
This is a package containing the Julia bindings for Enzyme. This is very much a work in progress and bug reports/discussion is greatly appreciated. Enzyme is a plugin that performs automatic differentiation (AD) of statically analyzable LLVM. It is highly-efficient and its ability perform AD on optimized code allows Enzyme to meet or exceed the performance of state-of-the-art AD tools. -
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LabPlot
Data Visualization and Analysis
LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone. -
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Catalyst.jl
Chemical reaction network and systems biology interface
Catalyst.jl is a symbolic modeling package for analysis and high-performance simulation of chemical reaction networks. Catalyst defines symbolic ReactionSystems, which can be created programmatically or easily specified using Catalyst's domain-specific language (DSL). Leveraging ModelingToolkit and Symbolics.jl, Catalyst enables large-scale simulations through auto-vectorization and parallelism. Symbolic ReactionSystems can be used to generate ModelingToolkit-based models, allowing the easy simulation and parameter estimation of mass action ODE models, Chemical Langevin SDE models, stochastic chemical kinetics jump process models, and more. Generated models can be used with solvers throughout the broader SciML ecosystem, including higher-level SciML packages (e.g. for sensitivity analysis, parameter estimation, machine learning applications, etc). -
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Julia VS Code
Julia extension for Visual Studio Code
This VS Code extension provides support for the Julia programming language. We build on Julia’s unique combination of ease-of-use and performance. Beginners and experts can build better software more quickly, and get to a result faster. With a completely live environment, Julia for VS Code aims to take the frustration and guesswork out of programming and put the fun back in. A hybrid “canvas programming” style combines the exploratory power of a notebook with the productivity and static analysis features of an IDE. VS Code is a powerful editor and customizable to your heart’s content (though the defaults are pretty good too). It has power features like multiple cursors, fuzzy file finding and Vim keybindings. -
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Latexify.jl
Convert julia objects to LaTeX equations, arrays or other environments
This is a package for generating LaTeX maths from Julia objects. This package utilizes Julia's homoiconicity to convert expressions to LaTeX-formatted strings. Latexify.jl supplies functionalities for converting a range of different Julia objects. -
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SciMLBase.jl
The Base interface of the SciML ecosystem
SciMLBase.jl is the core interface definition of the SciML ecosystem. It is a low-dependency library made to be depended on by the downstream libraries to supply the common interface and allow for the interexchange of mathematical problems. The SciML common interface ties together the numerical solvers of the Julia package ecosystem into a single unified interface. It is designed for maximal efficiency and parallelism, while incorporating essential features for large-scale scientific machine learning such as differentiability, composability, and sparsity. -
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The NLopt module for Julia
Package to call the NLopt nonlinear-optimization library from Julia
This module provides a Julia-language interface to the free/open-source NLopt library for nonlinear optimization. NLopt provides a common interface for many different optimization algorithms. -
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Tullio.jl
Tullio is a very flexible einsum macro
Tullio is a very flexible einsum macro. It understands many array operations written in index notation -- not just matrix multiplication and permutations, but also convolutions, stencils, scatter/gather, and broadcasting. Used by itself the macro writes ordinary nested loops much like Einsum.@einsum. One difference is that it can parse more expressions, and infer ranges for their indices. Another is that it will use multi-threading (via Threads.@spawn) and recursive tiling, on large enough arrays. -
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AbstractAlgebra.jl
Generic abstract algebra functionality in pure Julia
AbstractAlgebra is a pure Julia package for computational abstract algebra. It grew out of the Nemo project and provides all of the abstract types and generic implementations that Nemo relies on. It was originally developed by William Hart, Tommy Hofmann, Fredrik Johansson and Claus Fieker with contributions from others. Current maintainers are Claus Fieker, Tommy Hofmann and Max Horn. -
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BERT
Connector for Excel and the programming languages R and Julia
BERT is a tool for connecting Excel with the statistics language R. Specifically, it’s designed to support running R functions from Excel spreadsheet cells. In Excel terms, it’s for writing User-Defined Functions (UDFs) in R. All you have to do is write the function. Everything else – loading the function into Excel, managing parameters, and handling type conversion – is done automatically for you. It really could not be any easier. BERT also has a console that you can use to control Excel in real time, right from your R code. And (if you want), you can call R functions from VBA as well. -
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BetaML.jl
Beta Machine Learning Toolkit
The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, Python, R and any other language with a Julia binding. All models are implemented entirely in Julia and are hosted in the repository itself (i.e. they are not wrapper to third-party models). If your favorite option or model is missing, you can try to implement it yourself and open a pull request to share it (see the section Contribute below) or request its implementation. Thanks to its JIT compiler, Julia is indeed in the sweet spot where we can easily write models in a high-level language and still have them running efficiently. -
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CSV
Utility library for working with CSV and other delimited files
Welcome to CSV.jl! A pure-Julia package for handling delimited text data, be it comma-delimited (csv), tab-delimited (tsv), or otherwise. A fast, flexible delimited file reader/writer for Julia. -
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CUDA.jl
CUDA programming in Julia
High-performance GPU programming in a high-level language. JuliaGPU is a GitHub organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well-positioned to productively program hardware accelerators like GPUs without sacrificing performance. The latest development version of CUDA.jl requires Julia 1.8 or higher. If you are using an older version of Julia, you need to use a previous version of CUDA.jl. This will happen automatically when you install the package using Julia's package manager. -
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Comonicon
Your best CLI generator in JuliaLang
Roger's magic book for command line interfaces. -
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ComponentArrays.jl
Arrays with arbitrarily nested named components
The main export of this package is the ComponentArray type. "Components" of ComponentArrays are really just array blocks that can be accessed through a named index. This will create a new ComponentArray whose data is a view into the original, allowing for standalone models to be composed together by simple function composition. In essence, ComponentArrays allow you to do the things you would usually need a modeling language for, but without actually needing a modeling language. The main targets are for use in DifferentialEquations.jl and Optim.jl, but anything that requires flat vectors is fair game. -
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GMT.jl
Generic Mapping Tools Library Wrapper for Julia
The Generic Mapping Tools, GMT, is an open source collection of tools for manipulating geographic and Cartesian data sets (including filtering, trend fitting, gridding, projecting, etc.) and producing PostScript illustrations ranging from simple x–y plots via contour maps to artificially illuminated surfaces and 3D perspective views. This link will take you to an impressive collection of figures made with GMT. The GMT Julia wrapper was designed to work in a way the close as possible to the command line version and yet to provide all the facilities of the Julia language. In this sense, all GMT options are put in a single text string that is passed, plus the data itself when it applies, to the gmt() command. However, we also acknowledge that not every one is comfortable with the GMT syntax. This syntax is needed to accommodate the immense pool of options that let you control all details of a figure but that also makes it harder to read/master. -
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Mixed-effects models in Julia
A Julia package for fitting (statistical) mixed-effects models
This package defines linear mixed models (LinearMixedModel) and generalized linear mixed models (GeneralizedLinearMixedModel). Users can use the abstraction for statistical model API to build, fit (fit/fit!), and query the fitted models. A mixed-effects model is a statistical model for a response variable as a function of one or more covariates. For a categorical covariate the coefficients associated with the levels of the covariate are sometimes called effects, as in "the effect of using Treatment 1 versus the placebo". If the potential levels of the covariate are fixed and reproducible, e.g. the levels for Sex could be "F" and "M", they are modeled with fixed-effects parameters. If the levels constitute a sample from a population, e.g. the Subject or the Item at a particular observation, they are modeled as random effects.
