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  "Title": "Nonlinear Modeling of R-R Interval Dynamics",
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  "Description": "Automated and robust framework for analyzing R-R interval\n(RRi) signals using advanced nonlinear modeling and\npreprocessing techniques. The package implements a\ndual-logistic model to capture the rapid drop and subsequent\nrecovery of RRi during exercise, as described by\nCastillo-Aguilar et al. (2025)\n<doi:10.1038/s41598-025-93654-6>. In addition, 'CardioCurveR'\nincludes tools for filtering RRi signals using zero-phase\nButterworth low-pass filtering and for cleaning ectopic beats\nvia adaptive outlier replacement using local regression and\nrobust statistics. These integrated methods preserve the\ndynamic features of RRi signals and facilitate accurate\ncardiovascular monitoring and clinical research.",
  "License": "MIT + file LICENSE",
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    "author": "Matías Castillo <matcasti@umag.cl>",
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      "title": "Bootstrap RRi Model Parameter Estimates",
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    {
      "page": "CardioCurveR",
      "title": "CardioCurveR: Nonlinear Modeling and Preprocessing of R-R Interval Dynamics",
      "topics": [
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      ]
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      ]
    },
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