Showing 5 open source projects for "modular"

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  • 1
    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.
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  • 2
    plumber

    plumber

    Turn your R code into a web API

    plumber is an R package that enables rapid creation of HTTP APIs by decorating existing R functions with special roxygen-style comments. It transforms R scripts into RESTful web services with minimal setup and integrates easily with RStudio or CI environments.
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  • 3
    mlr3

    mlr3

    mlr3: Machine Learning in R - next generation

    mlr3 is a modern, object-oriented R framework for machine learning. It provides core abstractions (tasks, learners, resamplings, measures, pipelines) implemented using R6 classes, enabling extensible, composable machine learning workflows. It focuses on clean design, scalability (large datasets), and integration into the wider R ecosystem via extension packages. Users can do classification, regression, survival analysis, clustering, hyperparameter tuning, benchmarking etc., often via...
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  • 4
    Data Analysis for the Life Sciences

    Data Analysis for the Life Sciences

    Rmd source files for the HarvardX series PH525x

    ...The content covers topics such as data wrangling in R, statistical inference, genomics workflows, Bioconductor packages, and project-based analyses. Because it’s open and modular, contributors can suggest improvements, update modules, or add new exercises.
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  • 5
    DataScienceR

    DataScienceR

    a curated list of R tutorials for Data Science, NLP

    ...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, and reproducible workflows. The repository also shows examples of linking R with external resources — APIs, databases, and file formats — and integrating into larger pipelines. It acts as a learning scaffold for students or beginners transitioning to more advanced data science work in R, offering a hands-on, example-driven approach. ...
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