Showing 3 open source projects for "source code claude code"

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    Rezku Point of Sale

    Designed for Real-World Restaurant Operations

    Rezku is an all-inclusive ordering platform and management solution for all types of restaurant and bar concepts. You can now get a fully custom branded downloadable smartphone ordering app for your restaurant exclusively from Rezku.
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  • AestheticsPro Medical Spa Software Icon
    AestheticsPro Medical Spa Software

    Our new software release will dramatically improve your medspa business performance while enhancing the customer experience

    AestheticsPro is the most complete Aesthetics Software on the market today. HIPAA Cloud Compliant with electronic charting, integrated POS, targeted marketing and results driven reporting; AestheticsPro delivers the tools you need to manage your medical spa business. It is our mission To Provide an All-in-One Cutting Edge Software to the Aesthetics Industry.
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  • 1
    No-code system is for the visual creation of structural-functional models and the automatic generation of R language simulation models. The program can be used to describe information, production, organizational, and other processes. For graphical representation, the EdPM/EPM notation is used, which allowed us to implement: - structural-functional modeling using graphical methods; - the study of the efficiency of structural-functional models using simulation methods, that allow (e.g....
    Downloads: 0 This Week
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  • 2
    geocompr

    geocompr

    Geocomputation with R: an open source book

    This repository hosts the source for Geocomputation with R, an open-source book covering spatial data analysis, visualization, and modeling using R. It teaches how to work with vector and raster data, coordinate systems, mapping, and geocomputation techniques using packages like sf, terra, tmap, and more. Actively maintained and updated for real-world geospatial workflows.
    Downloads: 3 This Week
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  • 3
    Reproducible-research

    Reproducible-research

    A Reproducible Data Analysis Workflow with R Markdown, Git, Make, etc.

    In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible.
    Downloads: 0 This Week
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