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  "Title": "Continuous Development Models for Incremental Time-Series\nAnalysis",
  "Version": "0.1.3",
  "Date": "2018-05-01",
  "Author": "Bijan Seyednasrollah, Jennifer J. Swenson, Jean-Christophe\nDomec, James S. Clark",
  "Maintainer": "Bijan Seyednasrollah <bijan.s.nasr@gmail.com>",
  "Description": "Using the Bayesian state-space approach, we developed a\ncontinuous development model to quantify dynamic incremental\nchanges in the response variable. While the model was\noriginally developed for daily changes in forest green-up, the\nmodel can be used to predict any similar process. The CDM can\ncapture both timing and rate of nonlinear processes. Unlike\nstatics methods, which aggregate variations into a single\nmetric, our dynamic model tracks the changing impacts over\ntime. The CDM accommodates nonlinear responses to variation in\npredictors, which changes throughout development.",
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  "Encoding": "UTF-8",
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    "extra/citation.html",
    "extra/citation.json",
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    "getGibbsSummary",
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      "page": "fitCDM",
      "title": "Fit a CDM Model",
      "topics": [
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    },
    {
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    },
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    },
    {
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    },
    {
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      "topics": [
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    },
    {
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      "title": "Plot Posterior Distributions",
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    "rjags"
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      "title": "Getting started with phenoCDM",
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        "Installation",
        "Loading the package",
        "Example",
        "Simulating a dataset of three time-series:",
        "Plotting the simulated time-series:",
        "Fitting the CDM model on the simulated data:",
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        "Comparing the model fitted parameters against true values:",
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      "modified": "2018-05-02 02:45:15",
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