Showing 5 open source projects for "mining software linux"

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  • 1
    plotly

    plotly

    An interactive graphing library for R

    This part of the book teaches you how to leverage the plotly R package to create a variety of interactive graphics. There are two main ways to creating a plotly object: either by transforming a ggplot2 object (via ggplotly()) into a plotly object or by directly initializing a plotly object with plot_ly()/plot_geo()/plot_mapbox(). Both approaches have somewhat complementary strengths and weaknesses, so it can pay off to learn both approaches. Moreover, both approaches are an implementation of...
    Downloads: 0 This Week
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  • 2
    LabPlot

    LabPlot

    Data Visualization and Analysis

    LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone.
    Downloads: 29 This Week
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  • 3
    OmicSelector

    OmicSelector

    Feature selection and deep learning modeling for omic biomarker study

    OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant...
    Downloads: 1 This Week
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  • 4
    Reproducible-research

    Reproducible-research

    A Reproducible Data Analysis Workflow with R Markdown, Git, Make, etc.

    In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility....
    Downloads: 0 This Week
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  • 5
    Stats 337

    Stats 337

    Readings in applied data science

    Stats 337 is the repository for a Stanford discussion course titled Readings in Applied Data Science, taught in Spring 2018. Students were expected to read several papers or equivalent resources each week and discuss their implications in class. The reading list focuses on applied topics that the instructor considered important but often underrepresented in traditional data science curricula. Subjects include data collection, collaboration, software engineering, DevOps, teaching,...
    Downloads: 0 This Week
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