Showing 6 open source projects for "framework"

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    G-P - Global EOR Solution

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  • 1
    golem

    golem

    A Framework for Building Robust Shiny Apps

    golem is an opinionated framework for developing production-grade Shiny applications in R, treating the app like a full R package. It scaffolds project structure, testing, documentation, CI/CD, and supports containerization—streamlining the build-to-deploy pipeline while enforcing clean architecture and maintainability.
    Downloads: 0 This Week
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  • 2
    Shiny

    Shiny

    Build interactive web apps directly from R with Shiny framework

    Shiny is an R package from RStudio that enables users to build interactive web applications using R without requiring knowledge of JavaScript, HTML, or CSS. It allows statisticians and data scientists to turn their analyses into fully functional web dashboards with reactive elements, data inputs, visualizations, and controls, making data communication more effective and dynamic.
    Downloads: 0 This Week
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  • 3
    Statistical Rethinking 2024

    Statistical Rethinking 2024

    This course teaches data analysis

    ...This version is designed for students following the 2024 lecture series, offering the most current set of examples, exercises, and teaching material aligned with the Statistical Rethinking framework. Online, flipped instruction. I will pre-record the lectures each week. We'll meet online once a week for an hour to discuss the material. The discussion time (3-4pm Berlin Time) should allow people in the Americas to join in their morning.
    Downloads: 1 This Week
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  • 4
    AI-Agent-Host

    AI-Agent-Host

    The AI Agent Host is a module-based development environment.

    ...The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI Agent Host is built specifically for LangChain, a framework dedicated to developing applications powered by language models. LangChain recognizes that the most powerful and distinctive applications go beyond simply utilizing a language model and strive to be data-aware and agentic. Being data-aware involves connecting a language model to other sources of data, enabling a comprehensive understanding and analysis of information.
    Downloads: 0 This Week
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  • Smart Business Texting that Generates Pipeline Icon
    Smart Business Texting that Generates Pipeline

    Create and convert pipeline at scale through industry leading SMS campaigns, automation, and conversation management.

    TextUs is the leading text messaging service provider for businesses that want to engage in real-time conversations with customers, leads, employees and candidates. Text messaging is one of the most engaging ways to communicate with customers, candidates, employees and leads. 1:1, two-way messaging encourages response and engagement. Text messages help teams get 10x the response rate over phone and email. Business text messaging has become a more viable form of communication than traditional mediums. The TextUs user experience is intentionally designed to resemble the familiar SMS inbox, allowing users to easily manage contacts, conversations, and campaigns. Work right from your desktop with the TextUs web app or use the Chrome extension alongside your ATS or CRM. Leverage the mobile app for on-the-go sending and responding.
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  • 5

    Scripting Language Bindings

    A port of WFOPT to the several scripting languages

    This project contains bindings for various scripting languages to the Wheefun Options Parsing Library. It is meant to provide parity with the C implementation so .NET languages can take advantage of WFOPT. For more information, please see the main page.
    Downloads: 0 This Week
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  • 6
    benchm-ml

    benchm-ml

    A benchmark of commonly used open source implementations

    This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. ...
    Downloads: 0 This Week
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