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  "Description": "Contains functions to detect and visualise periods of\nclimate sensitivity (climate windows) for a given biological\nresponse. Please see van de Pol et al. (2016)\n<doi:10.1111/2041-210X.12590> and Bailey and van de Pol (2016)\n<doi:10.1371/journal.pone.0167980> for details.",
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      "topics": [
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      "topics": [
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    },
    {
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      "title": "Daily climate data since 1979.",
      "topics": [
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      "title": "Example output dataframe from function randwin.",
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      "title": "Merge two slidingwin analyses.",
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      "title": "Daily climate data since 2009.",
      "topics": [
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      "topics": [
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      "title": "Plot model beta estimates",
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      "title": "Visualise climate cross correlation or autocorrelation.",
      "topics": [
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      "page": "plotdelta",
      "title": "Plot deltaAICc of models",
      "topics": [
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    {
      "page": "plothist",
      "title": "Create a histogram of randomised deltaAICc values",
      "topics": [
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      "page": "plotweights",
      "title": "Plot distribution of model weights",
      "topics": [
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    },
    {
      "page": "plotwin",
      "title": "Plot the start and end time of best climate windows",
      "topics": [
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    {
      "page": "pvalue",
      "title": "Determine the probability that a given climate signal is 'true'.",
      "topics": [
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    {
      "page": "randwin",
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      "topics": [
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      "topics": [
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    {
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      "topics": [
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      "topics": [
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      "title": "Calculate within group means.",
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      "title": "Advanced climwin",
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      "engine": "knitr::rmarkdown",
      "headings": [
        "More complex baseline models",
        "Adding interactions with climate",
        "Mixed effects models",
        "Climate thresholds",
        "A worked example",
        "Biological measurements grouped across two years",
        "Spatial replication",
        "weightwin function",
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        "Why do we need climwin?",
        "slidingwin: The base function",
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        "randwin",
        "pvalue",
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