Showing 4 open source projects for "testing"

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  • 1
    RStudio

    RStudio

    RStudio is an integrated development environment (IDE) for R

    ...In addition to code editing and execution, RStudio offers extensive support for reproducible research via R Markdown, notebooks, and integration with version control systems like Git and SVN. Package development is built in, with tooling for building, checking, and testing R packages, plus integration with documentation tools, CRAN submission workflows, and project templates.
    Downloads: 78 This Week
    Last Update:
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  • 2
    Great Expectations

    Great Expectations

    Always know what to expect from your data

    Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams. Expectations are assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues. ...
    Downloads: 1 This Week
    Last Update:
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  • 3
    Cookiecutter Data Science

    Cookiecutter Data Science

    Project structure for doing and sharing data science work

    ...And we're not talking about bikeshedding the indentation aesthetics or pedantic formatting standards, ultimately, data science code quality is about correctness and reproducibility. It's no secret that good analyses are often the result of very scattershot and serendipitous explorations. Tentative experiments and rapidly testing approaches that might not work out are all part of the process for getting to the good stuff, and there is no magic bullet to turn data exploration into a simple, linear progression.
    Downloads: 0 This Week
    Last Update:
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  • 4
    MCPower

    MCPower

    MCPower — simple Monte Carlo power analysis for complex models

    ...It guides users through the full workflow across three tabs: Model setup (formula input with live parsing, CSV data upload with auto-detected variable types, effect size sliders, and correlation editing), Analysis configuration (find power for a given sample size or find the minimum sample size for a target power, with multiple testing correction and scenario analysis), and Results (interactive charts, exportable tables, and auto-generated Python replication scripts). Supports both standard linear models and mixed-effects models. Additional features include analysis history, configurable scenarios, and built-in documentation.
    Downloads: 24 This Week
    Last Update:
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