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  "Title": "Fast Change Point Detection via Sequential Gradient Descent",
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  "Authors@R": "c(\nperson(\"Xingchi\", \"Li\", , \"anthony.li.stat.tamu.edu@lixingchi.com\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0009-0006-2493-0853\")),\nperson(\"Xianyang\", \"Zhang\", , \"zhangxiany@stat.tamu.edu\", role = c(\"aut\", \"cph\"))\n)",
  "Description": "Implements fast change point detection algorithm based on\nthe paper \"Sequential Gradient Descent and Quasi-Newton's\nMethod for Change-Point Analysis\" by Xianyang Zhang, Trisha\nDawn <https://proceedings.mlr.press/v206/zhang23b.html>. The\nalgorithm is based on dynamic programming with pruning and\nsequential gradient descent. It is able to detect change points\na magnitude faster than the vanilla Pruned Exact Linear\nTime(PELT). The package includes examples of linear regression,\nlogistic regression, Poisson regression, penalized linear\nregression data, and whole lot more examples with custom cost\nfunction in case the user wants to use their own cost function.",
  "License": "Apache License (>= 2)",
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  "Date/Publication": "2026-04-20 01:06:09 UTC",
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  "Author": "Xingchi Li [aut, cre, cph] (ORCID:\n<https://orcid.org/0009-0006-2493-0853>),\nXianyang Zhang [aut, cph]",
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    "fastcpd.arma",
    "fastcpd.binomial",
    "fastcpd.garch",
    "fastcpd.lasso",
    "fastcpd.lm",
    "fastcpd.mean",
    "fastcpd.meanvariance",
    "fastcpd.mv",
    "fastcpd.poisson",
    "fastcpd.ts",
    "fastcpd.var",
    "fastcpd.variance",
    "plot",
    "print",
    "show",
    "summary",
    "variance_arma",
    "variance_lm",
    "variance_mean",
    "variance_median",
    "variance.arma",
    "variance.lm",
    "variance.mean",
    "variance.median"
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      "title": "Bitcoin Market Price (USD)",
      "object": "bitcoin",
      "class": [
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      ],
      "fields": [
        "date",
        "price"
      ],
      "rows": 1354,
      "table": true,
      "tojson": true
    },
    {
      "name": "occupancy",
      "title": "Occupancy Detection Data Set",
      "object": "occupancy",
      "class": [
        "data.frame"
      ],
      "fields": [
        "date",
        "Temperature",
        "Humidity",
        "Light",
        "CO2",
        "HumidityRatio",
        "Occupancy"
      ],
      "rows": 9752,
      "table": true,
      "tojson": true
    },
    {
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      "title": "Transcription Profiling of 57 Human Bladder Carcinoma Samples",
      "object": "transcriptome",
      "class": [
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        "4",
        "5",
        "6",
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    },
    {
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      "object": "uk_seatbelts",
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      "fields": [
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      "title": "Bitcoin Market Price (USD)",
      "topics": [
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    },
    {
      "page": "fastcpd",
      "title": "Find change points efficiently",
      "topics": [
        "fastcpd"
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    {
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      "topics": [
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        "fastcpd_ar"
      ]
    },
    {
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      "title": "Find change points efficiently in ARIMA(p, d, q) models",
      "topics": [
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        "fastcpd_arima"
      ]
    },
    {
      "page": "fastcpd_arma",
      "title": "Find change points efficiently in ARMA(p, q) models",
      "topics": [
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        "fastcpd_arma"
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    },
    {
      "page": "fastcpd_binomial",
      "title": "Find change points efficiently in logistic regression models",
      "topics": [
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        "fastcpd_binomial"
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    },
    {
      "page": "fastcpd_garch",
      "title": "Find change points efficiently in GARCH(p, q) models",
      "topics": [
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        "fastcpd_garch"
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    },
    {
      "page": "fastcpd_lasso",
      "title": "Find change points efficiently in penalized linear regression models",
      "topics": [
        "fastcpd.lasso",
        "fastcpd_lasso"
      ]
    },
    {
      "page": "fastcpd_lm",
      "title": "Find change points efficiently in linear regression models",
      "topics": [
        "fastcpd.lm",
        "fastcpd_lm"
      ]
    },
    {
      "page": "fastcpd_mean",
      "title": "Find change points efficiently in mean change models",
      "topics": [
        "fastcpd.mean",
        "fastcpd_mean"
      ]
    },
    {
      "page": "fastcpd_meanvariance",
      "title": "Find change points efficiently in mean variance change models",
      "topics": [
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        "fastcpd.mv",
        "fastcpd_meanvariance",
        "fastcpd_mv"
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    },
    {
      "page": "fastcpd_poisson",
      "title": "Find change points efficiently in Poisson regression models",
      "topics": [
        "fastcpd.poisson",
        "fastcpd_poisson"
      ]
    },
    {
      "page": "fastcpd_ts",
      "title": "Find change points efficiently in time series data",
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        "fastcpd.ts",
        "fastcpd_ts"
      ]
    },
    {
      "page": "fastcpd_var",
      "title": "Find change points efficiently in VAR(p) models",
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        "fastcpd_var"
      ]
    },
    {
      "page": "fastcpd_variance",
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      "topics": [
        "fastcpd.variance",
        "fastcpd_variance"
      ]
    },
    {
      "page": "fastcpd-class",
      "title": "An S4 class to store the output created with 'fastcpd()'",
      "topics": [
        "fastcpd-class"
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    },
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      "page": "occupancy",
      "title": "Occupancy Detection Data Set",
      "topics": [
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      ]
    },
    {
      "page": "plot",
      "title": "Plot the data and the change points for a fastcpd object",
      "topics": [
        "plot,fastcpd,missing-method",
        "plot.fastcpd"
      ]
    },
    {
      "page": "print",
      "title": "Print the call and the change points for a fastcpd object",
      "topics": [
        "print,fastcpd-method",
        "print.fastcpd"
      ]
    },
    {
      "page": "show",
      "title": "Show the available methods for a fastcpd object",
      "topics": [
        "show,fastcpd-method",
        "show.fastcpd"
      ]
    },
    {
      "page": "summary",
      "title": "Show the summary of a fastcpd object",
      "topics": [
        "summary,fastcpd-method",
        "summary.fastcpd"
      ]
    },
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      "topics": [
        "transcriptome"
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      "title": "UK Seatbelts Data",
      "topics": [
        "uk_seatbelts"
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    },
    {
      "page": "variance_arma",
      "title": "Variance estimation for ARMA model with change points",
      "topics": [
        "variance.arma",
        "variance_arma"
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    },
    {
      "page": "variance_lm",
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      "topics": [
        "variance.lm",
        "variance_lm"
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      "title": "Variance estimation for mean change models",
      "topics": [
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        "variance_mean"
      ]
    },
    {
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