Showing 5 open source projects for "windows api"

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  • 1
    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.
    Downloads: 2 This Week
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  • 2
    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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  • 3
    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...
    Downloads: 0 This Week
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  • 4
    performance

    performance

    Models' quality and performance metrics (R2, ICC, LOO, AIC, BF, ...)

    performance is part of the easystats ecosystem and offers model quality assessment tools for R. It computes metrics like R², RMSE, ICC, and conducts diagnostics such as overdispersion, zero‑inflation, convergence, and singularity checks, complementing model workflows with comprehensive evaluation.
    Downloads: 0 This Week
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  • 5
    future

    future

    R package: future: Unified Parallel and Distributed Processing in R

    The future package in R provides a unified abstraction for asynchronous and/or parallel computation. It allows R expressions to be scheduled for future evaluation, with the result retrieved later, in a way decoupled from the specific backend used. This lets code be written in a way that works with sequential execution, multicore, multisession, cluster, or remote compute backends, without changing the high-level code. It handles automatic exporting of needed global variables/functions,...
    Downloads: 0 This Week
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