Search Results for "software testing projects"

Showing 8 open source projects for "software testing projects"

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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
    devtools

    devtools

    Tools to make an R developer's life easier

    devtools is an R package designed to simplify R package development by providing functions for creating, building, testing, and installing packages from various sources (e.g., CRAN, GitHub). It integrates with usethis, roxygen2, testthat, and simplifies workflows for developers and contributors to the R ecosystem.
    Downloads: 1 This Week
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  • 3
    renv

    renv

    renv: Project environments for R

    renv is an R dependency management toolkit that enables project-level library isolation and reproducibility. It tracks package versions in a lockfile and can restore exact library states across machines or over time, making R projects portable and consistent.
    Downloads: 1 This Week
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  • 4
    ggthemes

    ggthemes

    Additional themes, scales, and geoms for ggplot2

    ggthemes is an R package that provides extra themes, scales, and geoms for ggplot2. It supplements the default ggplot2 offerings by allowing users to apply special themes (e.g., inspired by classic publications, external visualization styles), additional scale functions, and specialized geoms or color scales. It is often used to make ggplot2 plots adhere to aesthetic styles from famous news outlets, scientific journals, or presentation decks. Additional color scales and palettes for discrete...
    Downloads: 0 This Week
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  • 5
    box

    box

    Write reusable, composable and modular R code

    box is an R package providing a modular system / module loader for organizing reusable R code outside of full packages. It allows users to treat R scripts (files/folders) as modules — possibly nested — with explicit exports, imports, and scoping. The idea is to let users structure code in a more modular, composable way, without needing every reusable component to be a full CRAN-style package. It also provides a cleaner syntax for importing functions or modules (via box::use) that allows...
    Downloads: 0 This Week
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  • 6
    AI-Agent-Host

    AI-Agent-Host

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

    The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI...
    Downloads: 0 This Week
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  • 7
    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...
    Downloads: 0 This Week
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  • 8
    DataScienceR

    DataScienceR

    a curated list of R tutorials for Data Science, NLP

    The DataScienceR repository is a curated collection of tutorials, sample code, and project templates for learning data science using the R programming language. It includes an assortment of exercises, sample datasets, and instructional code that cover the core steps of a data science project: data ingestion, cleaning, exploratory analysis, modeling, evaluation, and visualization. Many of the modules demonstrate best practices in R, such as using the tidyverse, R Markdown, modular scripting,...
    Downloads: 0 This Week
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