Showing 3 open source projects for "high performance computing"

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  • 1
    easystats

    easystats

    The R easystats-project

    easystats is a meta‑package that installs and unifies a suite of R packages for post‑processing statistical models. It delivers a consistent API to assess model performance, effect sizes, parameters, and to generate reports and visualizations, all with minimal dependencies and maximum clarity.
    Downloads: 0 This Week
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  • 2
    data.table

    data.table

    Extends base R’s data for high-performance data manipulation

    data.table is an R package that extends base R’s data.frame for high-performance data manipulation. It offers concise syntax, blazing speed, and memory-efficient operations. It supports fast file reading/writing, joins, grouping, reshaping, and updates by reference. It is heavily used in large data workflows, big data in R, production pipelines, etc. Extremely efficient grouping/aggregation/summarization; can handle very large datasets (hundreds of millions to billions of rows) in memory (if available). ...
    Downloads: 0 This Week
    Last Update:
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  • 3
    RStan

    RStan

    RStan, the R interface to Stan

    RStan is the R interface to Stan, a C++ library for statistical modeling and high-performance statistical computation. It lets users specify models in the Stan modeling language (for Bayesian inference), compile them, and perform inference from R. Key inference approaches include full Bayesian inference via Hamiltonian Monte Carlo (specifically the No-U-Turn Sampler, NUTS), approximate Bayesian inference via variational methods, and optimization (penalized likelihood). ...
    Downloads: 2 This Week
    Last Update:
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