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  "Title": "Splitting-Coalescence-Estimation Method",
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  "Authors@R": "c(   person(\"Hannah\",\"Chazin\",role=c(\"aut\", \"cre\"),email = \"h.chazin@columbia.edu\"), \nperson(\"Soudeep\",\"Deb\",role=\"aut\",email = \"soudeep@iimb.ac.in\"),\nperson(\"Joshua\",\"Falk\",role=\"aut\"),\nperson(\"Arun\",\"Srinivasan\",role=\"aut\"),\nperson(\"Kyung Serk\",\"Cho\",role=\"aut\")   )",
  "Maintainer": "Hannah Chazin <h.chazin@columbia.edu>",
  "Description": "We introduce improved methods for statistically assessing\nbirth seasonality and intra-annual variation. The first method\nwe propose is a new idea that uses a nonparametric clustering\nprocedure to group individuals with similar time series data\nand estimate birth seasonality based on the clusters. One can\nuse the function SCEM() to implement this method. The second\nmethod estimates input parameters for use with a\npreviously-developed parametric approach (Tornero et al.,\n2013). The relevant code for this approach is makeFits_OLS(),\nwhile makeFits_initial() is the code to implement the same\nmethod but with given initial conditions for two parameters.\nThe latter can be used to show the disadvantage of the existing\napproach. One can use the function makeFits() to generate\nparametric birth seasonality estimates using either\ninitialization. Detailed description can be found here: Chazin\nHannah, Soudeep Deb, Joshua Falk, and Arun Srinivasan (2019)\n<doi:10.1111/arcm.12432> \"New Statistical Approaches to\nIntra-Individual Isotopic Analysis and Modeling Birth\nSeasonality in Studies of Herd Animals\".",
  "License": "GPL-3",
  "URL": "https://github.com/hchazin/SCEM, https://hchazin.github.io/SCEM/",
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  "Author": "Hannah Chazin [aut, cre], Soudeep Deb [aut], Joshua Falk [aut],\nArun Srinivasan [aut], Kyung Serk Cho [aut]",
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    {
      "page": "data-armenia",
      "title": "Armenia dataset",
      "topics": [
        "armenia"
      ]
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    {
      "page": "calculateRSS",
      "title": "Residual sum of squares (RSS) for all time series in a group.",
      "topics": [
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    },
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      "title": "Parameter estimates from a nonlinear lest squares (NLS) fit.",
      "topics": [
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      "title": "Bayesian Information Criterion (BIC) for a partition.",
      "topics": [
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      "title": "Estimates the trend function for a time series.",
      "topics": [
        "EstTrend"
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    },
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      "page": "iteration",
      "title": "Iteration step for the Splitting-Coalescence-Estimation Method (SCEM).",
      "topics": [
        "iteration"
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    },
    {
      "page": "kernel",
      "title": "Epanechnikov kernel",
      "topics": [
        "kernel"
      ]
    },
    {
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      "title": "Prepare results for cosine model fit.",
      "topics": [
        "makeFits"
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    {
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      "title": "Prepare results for cosine model fit with given initialization for two parameters.",
      "topics": [
        "makeFits_initial"
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    },
    {
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      "title": "Prepare results for cosine model fit with proposed initialization.",
      "topics": [
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      "title": "Splitting-Coalescence (SC) algorithm.",
      "topics": [
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    {
      "page": "SCEM",
      "title": "Splitting-Coalescence-Estimation Method (SCEM) for archaeological time series.",
      "topics": [
        "SCEM"
      ]
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    {
      "page": "sine_initial",
      "title": "Cosine model fitting with given initialization for two parameters.",
      "topics": [
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      "title": "Cosine model fitting with proposed initialization.",
      "topics": [
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        "Install the package",
        "Read the data file",
        "Implement the SCEM and Cosine method on the Armenia data",
        "Prepare relevant plots for the above analysis",
        "Use Armenia data to show the sensitivity to initialization (for Cosine method)"
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