Open Source Julia Software - Page 18
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Julia Software
Browse free open source Julia Software and projects below. Use the toggles on the left to filter open source Julia Software by OS, license, language, programming language, and project status.
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Bootstrap.jl
Statistical bootstrapping library for Julia
Bootstrapping is a widely applicable technique for statistical estimation. -
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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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ClimateMachine.jl
Earth System Model that automatically learns from data
The ClimateMachine is a software package that models the evolution of the Earth system over weeks to centuries. The ClimateMachine solves three-dimensional partial differential equations for the distributions of water, momentum, energy, and tracers such as carbon in the atmosphere, oceans, and on land. The ClimateMachine will harness a wide range of Earth observations and data generated computationally to predict the evolution of Earth’s climate and features such as droughts, rainfall extremes, and high-impact storms. -
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Coverage.jl
Take Julia code coverage and memory allocation results, do useful thin
Julia can track how many times, if any, each line of your code is run. This is useful for measuring how much of your code base your tests actually test, and can reveal the parts of your code that are not tested and might be hiding a bug. You can use Coverage.jl to summarize the results of this tracking or to send them to a service like Coveralls.io or Codecov.io. Julia can track how much memory is allocated by each line of your code. This can reveal problems like type instability, or operations that you might have thought were cheap (in terms of memory allocated) but aren't (i.e. accidental copying). -
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Cxx.jl
The Julia C++ Interface
The Julia C++ Foreign Function Interface (FFI) and REPL. Now, this package provides an out-of-box installation experience on 64-bit Linux, macOS and Windows. -
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DSGE.jl
Solve and estimate Dynamic Stochastic General Equilibrium models
DSGE.jl is a Julia package developed by the Federal Reserve Bank of New York for estimating and analyzing dynamic stochastic general equilibrium (DSGE) models. It provides tools for Bayesian estimation, filtering, forecasting, and model comparison, supporting both academic research and policy applications. DSGE.jl includes pre-configured models used by central banks and offers extensibility for custom macroeconomic modeling. -
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Dash for Julia
A Julia interface to the Dash ecosystem for creating analytic web apps
Create beautiful, analytic applications in Julia. Built on top of Plotly.js, React and HTTP.jl, Dash ties modern UI elements like dropdowns, sliders, and graphs directly to your analytical Julia code. -
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Diana.jl
GraphQL for Julia
GraphQL is a data query language for APIs and a runtime for fulfilling those queries with your existing data developed by Facebook in 2012 and launched on the internet in 2015. It is an alternative to REST and ad-hoc web service architectures. -
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DocOpt.jl
Command line arguments parser
DocOpt.jl is a port of docopt written in the Julia language. docopt generates a command-line arguments parser from human-readable usage patterns. The DocOpt module exports just one function, docopt, which takes multiple arguments but all of them except the first one are optional. -
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FlatWorld
2D physics simulator in Julia language
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Franklin.jl
Static site generator. Simple, customisable, fast, maths with KaTeX
Franklin is a simple static site generator (SSG) oriented towards technical blogging (code, maths, ...), flexibility and extensibility. The base syntax is plain markdown with a few extensions such as the ability to define and use LaTeX-like commands in or outside of maths environments and the possibility to evaluate code blocks on the fly. -
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General
The official registry of general Julia packages
General is the default package registry for the Julia programming language, providing the foundation for Julia’s package manager, Pkg.jl. It stores essential information about packages, including versions, dependencies, and compatibility constraints, and serves as the central hub for the Julia package ecosystem. The registry is open to all and makes it easy for developers and researchers to access, install, and share packages across a wide range of domains. New packages and updates are added through pull requests, often automated via Registrator.jl, with qualifying requests merged automatically while others undergo manual review. The system also integrates with TagBot to automate tagging of package releases once registered. By maintaining clear rules for licensing and contribution, General ensures a reliable and transparent process for managing Julia’s open source package ecosystem. -
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GeophysicalFlows.jl
Geophysical fluid dynamics pseudospectral solvers with Julia
GeophysicalFlows.jl is a collection of modules that leverage the FourierFlows.jl framework to provide solvers for problems in Geophysical Fluid Dynamics, on periodic domains using Fourier-based pseudospectral methods. -
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GraphNeuralNetworks.jl
Graph Neural Networks in Julia
GraphNeuralNetworks.jl is a graph neural network library written in Julia and based on the deep learning framework Flux.jl. -
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Hecke.jl
Computational algebraic number theory
Hecke is a software package for algebraic number theory maintained by Claus Fieker, Tommy Hofmann and Carlo Sircana. It is written in julia and is based on the computer algebra packages Nemo and AbstractAlgebra. Hecke is part of the OSCAR project and the development is supported by the Deutsche Forschungsgemeinschaft DFG within the Collaborative Research Center TRR 195. -
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IJulia.jl
Julia kernel for Jupyter
IJulia is a Julia-language backend (kernel) for Jupyter notebooks, allowing users to write and execute Julia code interactively in browser-based notebooks. It integrates seamlessly with Jupyter’s ecosystem, supporting markdown, plotting, multimedia, and inline output. IJulia is ideal for scientific computing, data analysis, and education, combining the power of Julia with the interactive capabilities of Jupyter. -
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ImplicitGlobalGrid.jl
Distributed parallelization of stencil-based GPU and CPU applications
ImplicitGlobalGrid is an outcome of a collaboration of the Swiss National Supercomputing Centre, ETH Zurich (Dr. Samuel Omlin) with Stanford University (Dr. Ludovic Räss) and the Swiss Geocomputing Centre (Prof. Yuri Podladchikov). It renders the distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid almost trivial and enables close to ideal weak scaling of real-world applications on thousands of GPUs [1, 2, 3]. ImplicitGlobalGrid relies on the Julia MPI wrapper (MPI.jl) to perform halo updates close to hardware limit and leverages CUDA-aware or ROCm-aware MPI for GPU-applications. The communication can straightforwardly be hidden behind computation [1, 3] (how this can be done automatically when using ParallelStencil.jl is shown in; a general approach particularly suited for CUDA C applications is explained in. -
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IndexedTables.jl
Flexible tables with ordered indices
IndexedTables provides tabular data structures where some of the columns form a sorted index. It provides the backend to JuliaDB, but can be used on its own for efficient in-memory data processing and analytics. -
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IntervalArithmetic.jl
Library for validated numerics using interval arithmetic
IntervalArithmetic.jl is a Julia package for validated numerics in Julia. All calculations are carried out using interval arithmetic where quantities are treated as intervals. The final result is a rigorous enclosure of the true value. We are working towards having the IntervalArithmetic library be conformant with the IEEE 1788-2015 Standard for Interval Arithmetic. To do so, we have incorporated tests from the ITF1788 test suite. -
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Iterative Solvers
Iterative algorithms for solving linear systems, eigensystems
IterativeSolvers is a Julia package that provides iterative algorithms for solving linear systems, eigensystems, and singular value problems. -
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Julia Data Science
Book on Julia for Data Science
This is an open source and open access book on how to do Data Science using Julia. Our target audience are researchers from all fields of applied sciences. Of course, we hope to be useful for industry too. You can navigate through the pages of the ebook by using the arrow keys (left/right) on your keyboard. -
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JuliaCall for Seamless Integration of R
Embed Julia in R
Package JuliaCall is an R interface to Julia, which is a high-level, high-performance dynamic programming language for numerical computing. Below is an image for Mandelbrot set. JuliaCall brings more than 100 times speedup of the calculation. -
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KernelDensity.jl
Kernel density estimators for Julia
Kernel density estimators for Julia. -
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Knet
Koç University deep learning framework
Knet.jl is a deep learning package implemented in Julia, so you should be able to run it on any machine that can run Julia. It has been extensively tested on Linux machines with NVIDIA GPUs and CUDA libraries, and it has been reported to work on OSX and Windows. If you would like to try it on your own computer, please follow the instructions on Installation. If you would like to try working with a GPU and do not have access to one, take a look at Using Amazon AWS or Using Microsoft Azure. If you find a bug, please open a GitHub issue. If you don't have access to a GPU machine, but would like to experiment with one, Amazon Web Services is a possible solution. I have prepared a machine image (AMI) with everything you need to run Knet. Here are step-by-step instructions for launching a GPU instance with a Knet image (the screens may have changed slightly since this writing). -
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LightGraphs
An optimized graphs package for the Julia programming language
LightGraphs offers both (a) a set of simple, concrete graph implementations -- Graph (for undirected graphs) and DiGraph (for directed graphs), and (b) an API for the development of more sophisticated graph implementations under the AbstractGraph type. The project goal is to mirror the functionality of robust network and graph analysis libraries such as NetworkX while being simpler to use and more efficient than existing Julian graph libraries such as Graphs.jl. It is an explicit design decision that any data not required for graph manipulation (attributes and other information, for example) is expected to be stored outside of the graph structure itself. Such data lends itself to storage in more traditional and better-optimized mechanisms.
