Showing 4 open source projects for "icon based programming language"

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  • 1
    Circuitscape.jl

    Circuitscape.jl

    Algorithms from circuit theory to predict connectivity

    Circuitscape is an open-source program that uses circuit theory to model connectivity in heterogeneous landscapes. Its most common applications include modeling the movement and gene flow of plants and animals, as well as identifying areas important for connectivity conservation. The new Circuitscape is built entirely in the Julia language, a new programming language for technical computing. Julia is built from the ground up to be fast. As such, this offers a number of advantages over the...
    Downloads: 8 This Week
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  • 2
    Makie

    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...
    Downloads: 11 This Week
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  • 3
    Gridap.jl

    Gridap.jl

    Grid-based approximation of partial differential equations in Julia

    Gridap provides a set of tools for the grid-based approximation of partial differential equations (PDEs) written in the Julia programming language. The library currently supports linear and nonlinear PDE systems for scalar and vector fields, single and multi-field problems, conforming and nonconforming finite element (FE) discretizations, on structured and unstructured meshes of simplices and n-cubes.
    Downloads: 10 This Week
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  • 4
    Bayesian Julia

    Bayesian Julia

    Bayesian Statistics using Julia and Turing

    Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from...
    Downloads: 7 This Week
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