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  "Title": "Perform Linear or Nonlinear Cross Lag Analysis",
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  "Description": "Linear or nonlinear cross-lagged panel model can be built\nfrom input data. Users can choose the appropriate method from\nthree methods for constructing nonlinear cross lagged models.\nThese three methods include polynomial regression, generalized\nadditive model and generalized linear mixed model.In addition,\na function for determining linear relationships is provided.\nRelevant knowledge of cross lagged models can be learned\nthrough the paper by Fredrik Falkenström (2024)\n<doi:10.1016/j.cpr.2024.102435> and the paper by A Gasparrini\n(2010) <doi:10.1002/sim.3940>.",
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      "page": "adjust_target",
      "title": "Title Generate covariates' residuals",
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      "title": "Title Nonlinear Cross Lag Analysis",
      "topics": [
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    {
      "page": "clpm_gam_r",
      "title": "Title Nonlinear Cross Lag Analysis: autoregression",
      "topics": [
        "clpm_gam_r"
      ]
    },
    {
      "page": "clpm_gamm4_c",
      "title": "Title Cross-lag analysis based on generalized additive mixture models",
      "topics": [
        "clpm_gamm4_c"
      ]
    },
    {
      "page": "clpm_gamm4_r",
      "title": "Title Cross-lag analysis based on generalized additive mixture models: autoregression",
      "topics": [
        "clpm_gamm4_r"
      ]
    },
    {
      "page": "clpm_poly_c",
      "title": "Title Nonlinear Cross Lag Analysis based on Polynomial linear regression",
      "topics": [
        "clpm_poly_c"
      ]
    },
    {
      "page": "clpm_poly_r",
      "title": "Title Nonlinear Cross Lag Analysis based on Polynomial linear regression: autoregression",
      "topics": [
        "clpm_poly_r"
      ]
    },
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      "title": "Title Linear Cross lagged analysis",
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      ]
    },
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      "title": "Title Determine a linear relationship between x and y by Restricted Cubic Splines(RCS) and plot it",
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        "judge_line"
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