Showing 5 open source projects for "statistical"

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    Realistic Workplace Simulations that Show Applicant Skills in Action

    Skillfully transforms hiring through AI-powered skill simulations that show you how candidates actually perform before you hire them. Our platform helps companies cut through AI-generated resumes and rehearsed interviews by validating real capabilities in action. Through dynamic job specific simulations and skill-based assessments, companies like Bloomberg and McKinsey have cut screening time by 50% while dramatically improving hire quality.
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  • ShareCRM is an AI-powered enterprise CRM platform designed to connect data and teams across the entire customer lifecycle. Icon
    ShareCRM is an AI-powered enterprise CRM platform designed to connect data and teams across the entire customer lifecycle.

    Trusted by 6000+ Large and Medium Enterprises

    ShareCRM is an AI-powered, customizable and affordable enterprise CRM solution to seamlessly integrate and empower every aspect of your business.
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  • 1
    stdlib

    stdlib

    Standard library for JavaScript and Node.js

    A standard library for javascript and node.js. High performance, rigorous, and robust mathematical and statistical functions. Build advanced statistical models and machine learning libraries. Plotting and graphics functionality for data visualization and exploratory data analysis. Analyze and understand your data. Comprehensively tested utilities for application and library development. Functions to assert, group, filter, map, pluck, and transform your data both in browsers and on the server. ...
    Downloads: 9 This Week
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  • 2
    plotly.js

    plotly.js

    JavaScript charting library behind Plotly and Dash

    Plotly JavaScript Open Source Graphing Library. Built on top of d3.js and stack.gl, Plotly.js is a high-level, declarative charting library. plotly.js ships with over 40 chart types, including 3D charts, statistical graphs, and SVG maps. plotly.js is free and open source and you can view the source, report issues or contribute on GitHub. For plotly.js to build with Webpack you will need to install ify-loader@v1.1.0+ and add it to your webpack.config.json. This adds Browserify transform compatibility to Webpack which is necessary for some plotly.js dependencies. ...
    Downloads: 9 This Week
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  • 3
    ARC-AGI

    ARC-AGI

    The Abstraction and Reasoning Corpus

    ...The dataset is structured as grid-based puzzles, where each task requires understanding transformations such as symmetry, counting, or spatial manipulation. Unlike traditional machine learning benchmarks, ARC emphasizes generalization and reasoning over statistical pattern recognition, making it particularly challenging for current AI systems. The repository also includes a browser-based interface that allows humans to attempt solving the tasks manually, providing a baseline for comparison.
    Downloads: 1 This Week
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  • 4
    Hiper

    Hiper

    A statistical analysis tool for performance testing

    Hiper is an open-source command-line tool designed for statistical analysis of web performance and page load behavior during performance testing. The tool repeatedly loads a specified webpage multiple times and gathers detailed timing metrics in order to produce more reliable performance measurements than single-run benchmarks. By averaging data across multiple page loads, Hiper helps developers understand whether performance optimizations actually improve real-world page loading behavior. ...
    Downloads: 0 This Week
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  • Dynamic Work and Complex Project Management Platform | Quickbase Icon
    Dynamic Work and Complex Project Management Platform | Quickbase

    Quickbase is the leading application platform for dynamic work.

    Our no-code platform lets you easily create, connect, and customize enterprise applications that fix visibility and workflow gaps without replacing a single system.
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  • 5
    PadotusRNDT.js

    PadotusRNDT.js

    This library allows you to generate a random number

    This library allows you to generate a random number in the specified range from 0 to n, without using the built-in function Math.random (), and gets a random number from the processing time of polymorphic mathematical calculations, which depends on the current physical parameters of the CPU, RAM, and t .P. The resulting sequence corresponds to a discrete uniform distribution and is close to natural random sequences in terms of statistical randomness tests (LFSR, Approximate Entropy, DIEHARD Test).
    Downloads: 0 This Week
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