Showing 4 open source projects for "linux basic programming"

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  • 1
    purrr

    purrr

    A functional programming toolkit for R

    purrr enhances R’s functional programming capabilities by providing a consistent set of tools for working with lists and vectors, enabling safer and more expressive iteration compared to base R’s loop functions.
    Downloads: 1 This Week
    Last Update:
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  • 2
    R Source

    R Source

    Read-only mirror of R source code

    The wch/r-source repository is a read-only mirror of the official R language source code, maintained to reflect the upstream Subversion (SVN) R core development tree. This mirror provides public visibility into R’s internals—everything from the interpreter, base and recommended packages, documentation, and C/Fortran code under the hood. It is updated hourly to stay in sync with the upstream SVN. Although it mirrors the R source for browsing and reference, it is not the “canonical development...
    Downloads: 3 This Week
    Last Update:
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  • 3
    Awesome Network Analysis

    Awesome Network Analysis

    A curated list of awesome network analysis resources

    awesome-network-analysis is a curated list of resources focused on network and graph analysis, including libraries, frameworks, visualization tools, datasets, and academic papers. It covers multiple programming languages and domains like sociology, biology, and computer science. This repository serves as a central reference for researchers, analysts, and developers working with network data.
    Downloads: 3 This Week
    Last Update:
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  • 4
    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
    Last Update:
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