Showing 6 open source projects for "parallel"

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  • 1
    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.
    Downloads: 3 This Week
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  • 2
    targets

    targets

    Function-oriented Make-like declarative workflows for R

    The targets package is a pipeline / workflow management tool in R, designed to coordinate multi‐step computational workflows in data science / statistics. It tracks dependencies between “targets” (computational steps), skips steps whose upstream data or code hasn’t changed, supports parallel computation, branching (dynamic generation of sub‐targets), file format abstractions, and encourages reproducible and efficient analyses. It’s something like GNU Make for R, but more integrated. Skipping computation for up-to-date targets so that unchanged parts of the workflow are not recomputed. Targets can represent files or R objects, and tracking file changes etc is incorporated.
    Downloads: 0 This Week
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  • 3
    ggraph

    ggraph

    Grammar of Graph Graphics

    ggraph adapts the Grammar of Graphics from ggplot2 for network and graph visualizations. It integrates with tidygraph/igraph data structures, providing a wide range of geoms, layouts (e.g. hive plots, circle packing), and layering methods tailored to hierarchical or relational data.
    Downloads: 0 This Week
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  • 4
    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...
    Downloads: 0 This Week
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  • 5
    Statistical Rethinking 2023

    Statistical Rethinking 2023

    Statistical Rethinking Course for Jan-Mar 2023

    The 2023 edition modernizes and expands on the same curriculum, adjusting exercises and code for newer versions of R, Stan, and supporting packages. It continues to provide scripts for lectures and tutorials, while integrating refinements to examples, notation, and computational workflows introduced that year. Compared with 2022, some models are rewritten for clarity, and teaching materials reflect refinements in McElreath’s evolving presentation of Bayesian data analysis. Students following...
    Downloads: 0 This Week
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  • 6
    Statistical Rethinking 2022

    Statistical Rethinking 2022

    Statistical Rethinking course winter 2022

    ...The code emphasizes Bayesian data analysis using R, the rethinking package, and Stan models. It includes lecture code files, example datasets, and structured exercises that parallel the topics covered in the lectures (probability, regression, model comparison, Bayesian updating). The repo functions as a direct hands-on reference for students following the 2022 recorded lecture series. There are 10 weeks of instruction. Links to lecture recordings will appear in this table. Weekly problem sets are assigned on Fridays and due the next Friday, when we discuss the solutions in the weekly online meeting.
    Downloads: 1 This Week
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