Showing 4 open source projects for "data visualization, research"

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    TEACUP

    TEACUP

    TCP Experiment Automation Controlled Using Python

    TEACUP automates many aspects of running TCP performance experiments in a specially-constructed physical testbed. TEACUP enables repeatable testing of different TCP algorithms over a range of emulated network path conditions, bottleneck rate limits and bottleneck queuing disciplines. TEACUP utilises a text-based configuration file to define experiments as combinations of parameters specifying desired network path and end host conditions. When multiple values are provided (e.g. for TCP...
    Downloads: 1 This Week
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  • 2

    runjags

    The 'runjags' R package and standalone JAGS extension module

    This package provides high-level interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A...
    Downloads: 1 This Week
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  • 3
    SimAssem

    SimAssem

    For processing and/or simulating species assemblage data.

    SimAssem is a tool for investigating the performances of species richness estimators across widely ranging assemblages and survey designs. Assemblages are simulated by specifying the: 1) number of species, 2) total abundance of individuals, 3) abundance distribution, 4) spatial configuration, 5) species detection probabilities, 6) survey design, and 7) number of grid cells to survey (out of a square grid comprised of 10,000 cells). SimAssem also processes existing encounter data and formats...
    Downloads: 0 This Week
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  • 4
    YANG J. (Yet Another Network Generator)
    YANG (Yet Another Network Generator - Java) enables you to generate social networks given various social rules observed in the real population. Uses: generate realistic networks to be used in individual-centric models, teaching or benchmarking.
    Downloads: 2 This Week
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