{
  "_id": "6a1d42041d7bb097a0a40dd2",
  "Package": "dynr",
  "Date": "2025-09-02",
  "Title": "Dynamic Models with Regime-Switching",
  "Authors@R": "c(person(\"Lu\", \"Ou\", role=\"aut\"),\nperson(c(\"Michael\", \"D.\"), \"Hunter\", role=c(\"aut\", \"cre\"), email=\"mike.dynr@gmail.com\", comment=c(ORCID = \"0000-0002-3651-6709\")),\nperson(\"Sy-Miin\", \"Chow\", role=\"aut\", comment=c(ORCID = \"0000-0003-1938-027X\")),\nperson(\"Linying\", \"Ji\", role=\"aut\", email=\"\"),\nperson(\"Meng\", \"Chen\", role=\"aut\", email=\"\"),\nperson(\"Hui-Ju\", \"Hung\", role=\"aut\", email=\"\"),\nperson(\"Jungmin\", \"Lee\", role=\"aut\", email=\"leejapply@gmail.com\"),\nperson(\"Yanling\", \"Li\", role=\"aut\", email=\"\"),\nperson(\"Jonathan\", \"Park\", role=\"aut\", email=\"\"),\nperson(\"Massachusetts Institute of Technology\", role=\"cph\"),\nperson(\"S. G.\", \"Johnson\", role=\"cph\"),\nperson(\"Benoit\", \"Scherrer\", role=\"cph\"),\nperson(\"Dieter\", \"Kraft\", role=\"cph\"))",
  "Maintainer": "Michael D. Hunter <mike.dynr@gmail.com>",
  "URL": "https://dynrr.github.io/, https://github.com/mhunter1/dynr",
  "Contact": "<dynr@googlegroups.com>",
  "VignetteBuilder": "knitr",
  "Description": "Intensive longitudinal data have become increasingly\nprevalent in various scientific disciplines. Many such data\nsets are noisy, multivariate, and multi-subject in nature. The\nchange functions may also be continuous, or continuous but\ninterspersed with periods of discontinuities (i.e., showing\nregime switches). The package 'dynr' (Dynamic Modeling in R) is\nan R package that implements a set of computationally efficient\nalgorithms for handling a broad class of linear and nonlinear\ndiscrete- and continuous-time models with regime-switching\nproperties under the constraint of linear Gaussian measurement\nfunctions. The discrete-time models can generally take on the\nform of a state-space or difference equation model. The\ncontinuous-time models are generally expressed as a set of\nordinary or stochastic differential equations. All estimation\nand computations are performed in C, but users are provided\nwith the option to specify the model of interest via a set of\nsimple and easy-to-learn model specification functions in R.\nModel fitting can be performed using single-subject time series\ndata or multiple-subject longitudinal data. Ou, Hunter, & Chow\n(2019) <doi:10.32614%2FRJ-2019-012> provided a detailed\nintroduction to the interface and more information on the\nalgorithms.",
  "SystemRequirements": "GNU make",
  "NeedsCompilation": "yes",
  "License": "GPL-3",
  "LazyLoad": "yes",
  "LazyData": "yes",
  "Collate": "'dynrData.R' 'dynrRecipe.R' 'dynrModelInternal.R'\n'dynrModel.R' 'dynrCook.R' 'dynrPlot.R' 'dynrFuncAddress.R'\n'dynrMi.R' 'dynrTaste.R' 'dynrVersion.R' 'dataDoc.R'\n'dynrGetDerivs.R' 'dynrPredict.R'",
  "RdMacros": "Rdpack",
  "Version": "0.1.16-114",
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  "RoxygenNote": "5.0.1",
  "Packaged": {
    "Date": "2026-06-01 08:20:56 UTC",
    "User": "root"
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  "Author": "Lu Ou [aut], Michael D. Hunter [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-3651-6709>), Sy-Miin Chow [aut]\n(ORCID: <https://orcid.org/0000-0003-1938-027X>), Linying Ji\n[aut], Meng Chen [aut], Hui-Ju Hung [aut], Jungmin Lee [aut],\nYanling Li [aut], Jonathan Park [aut], Massachusetts Institute\nof Technology [cph], S. G. Johnson [cph], Benoit Scherrer\n[cph], Dieter Kraft [cph]",
  "Config/pak/sysreqs": "make",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2025-09-02 16:42:15 UTC",
  "RemoteUrl": "https://github.com/cran/dynr",
  "RemoteRef": "HEAD",
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  "MD5sum": "b2a5e40cc324559544dd47b107386f9c",
  "_user": "cran",
  "_type": "src",
  "_file": "dynr_0.1.16-114.tar.gz",
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  "_created": "2026-06-01T08:20:56.000Z",
  "_published": "2026-06-01T08:25:40.425Z",
  "_distro": "noble",
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  "_buildurl": "https://github.com/r-universe/cran/actions/runs/26743245960",
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  "_commit": {
    "id": "010124e524cd4ef031d5706543645401d9ae04a3",
    "author": "Michael D. Hunter <mike.dynr@gmail.com>",
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    "message": "version 0.1.16-114\n",
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  "_assets": [
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      "date": "2017-01-09"
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      "date": "2018-09-24"
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      "date": "2019-10-05"
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      "date": "2020-02-11"
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      "date": "2021-02-20"
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  "_exports": [
    "coef<-",
    "diag",
    "dynr.config",
    "dynr.cook",
    "dynr.data",
    "dynr.flowField",
    "dynr.ggplot",
    "dynr.ldl",
    "dynr.mi",
    "dynr.model",
    "dynr.plotFreq",
    "dynr.taste",
    "dynr.taste2",
    "dynr.trajectory",
    "dynr.version",
    "getdx",
    "plotFormula",
    "plotGCV",
    "prep.formulaDynamics",
    "prep.initial",
    "prep.loadings",
    "prep.matrixDynamics",
    "prep.measurement",
    "prep.noise",
    "prep.regimes",
    "prep.tfun",
    "print",
    "printex",
    "show",
    "theta_plot"
  ],
  "_datasets": [
    {
      "name": "EMG",
      "title": "Single-subject time series of facial electromyography data",
      "object": "EMG",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "time",
        "iEMG",
        "SelfReport"
      ],
      "rows": 695,
      "table": true,
      "tojson": true
    },
    {
      "name": "EMGsim",
      "title": "Simulated single-subject time series to capture features of facial electromyography data",
      "object": "EMGsim",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "time",
        "EMG",
        "self",
        "truestate",
        "trueregime"
      ],
      "rows": 500,
      "table": true,
      "tojson": true
    },
    {
      "name": "LinearOsc",
      "title": "Simulated time series data for a deterministic linear damped oscillator model",
      "object": "LinearOsc",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "theTimes",
        "x"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "LogisticSetPointSDE",
      "title": "Simulated time series data for a stochastic linear damped oscillator model with logistic time-varying setpoints",
      "object": "LogisticSetPointSDE",
      "class": [
        "data.frame"
      ],
      "fields": [
        "x",
        "y",
        "z",
        "id",
        "obsy",
        "times"
      ],
      "rows": 2410,
      "table": true,
      "tojson": true
    },
    {
      "name": "NonlinearDFAsim",
      "title": "Simulated multi-subject time series based on a dynamic factor analysis model with nonlinear relations at the latent level",
      "object": "NonlinearDFAsim",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "time",
        "y1",
        "y2",
        "y3",
        "y4",
        "y5",
        "y6"
      ],
      "rows": 3000,
      "table": true,
      "tojson": true
    },
    {
      "name": "oscData",
      "title": "Another simulated multilevel multi-subject time series of a damped oscillator model",
      "object": "oscData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "times",
        "u1",
        "u2",
        "y1",
        "trueb"
      ],
      "rows": 1800,
      "table": true,
      "tojson": true
    },
    {
      "name": "Oscillator",
      "title": "Simulated time series data of a damped linear oscillator",
      "object": "Oscillator",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "y1",
        "times",
        "x1",
        "x2"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "Outliers",
      "title": "Simulated time series data for detecting outliers.",
      "object": "Outliers",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
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      "object": "PFAsim",
      "class": [
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      ],
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        "Time",
        "V1",
        "V2",
        "V3",
        "V4",
        "V5",
        "V6",
        "F1",
        "F2"
      ],
      "rows": 2500,
      "table": true,
      "tojson": true
    },
    {
      "name": "PPsim",
      "title": "Simulated time series data for multiple eco-systems based on a predator-and-prey model",
      "object": "PPsim",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "time",
        "prey",
        "predator",
        "x",
        "y"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "RSPPsim",
      "title": "Simulated time series data for multiple eco-systems based on a regime-switching predator-and-prey model",
      "object": "RSPPsim",
      "class": [
        "data.frame"
      ],
      "fields": [
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        "prey",
        "predator",
        "x",
        "y",
        "cond",
        "regime"
      ],
      "rows": 15000,
      "table": true,
      "tojson": true
    },
    {
      "name": "TrueInit_Y14",
      "title": "Simulated multilevel multi-subject time series of a Van der Pol Oscillator",
      "object": "TrueInit_Y14",
      "class": [
        "data.frame"
      ],
      "fields": [
        "batch",
        "kk",
        "trueInit",
        "id",
        "time",
        "y1",
        "y2",
        "y3",
        "co1",
        "co2"
      ],
      "rows": 60000,
      "table": true,
      "tojson": true
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      "name": "VARsim",
      "title": "Simulated time series data for multiple imputation in dynamic modeling.",
      "object": "VARsim",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "Time",
        "ca",
        "cn",
        "wp",
        "hp",
        "x1",
        "x2"
      ],
      "rows": 10000,
      "table": true,
      "tojson": true
    },
    {
      "name": "vdpData",
      "title": "Another simulated multilevel multi-subject time series of a Van der Pol Oscillator",
      "object": "vdpData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "batch",
        "kk",
        "trueInit",
        "time",
        "y1",
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        "trueb",
        "id"
      ],
      "rows": 10000,
      "table": true,
      "tojson": true
    }
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  "_help": [
    {
      "page": "dynr-package",
      "title": "Dynamic Models with Regime-Switching",
      "concept": [
        "State-space modeling",
        "dynamic model",
        "differential equation",
        "regime switching",
        "nonlinear",
        "Time series"
      ],
      "topics": [
        "dynr-package",
        "dynr"
      ]
    },
    {
      "page": "autoplot.dynrTaste",
      "title": "The ggplot of the outliers estimates.",
      "topics": [
        "autoplot.dynrTaste"
      ]
    },
    {
      "page": "coef.dynrCook",
      "title": "Extract fitted parameters from a dynrCook Object",
      "topics": [
        "coef.dynrCook",
        "coef.dynrModel",
        "coef<-",
        "coef<-.dynrModel"
      ]
    },
    {
      "page": "confint.dynrCook",
      "title": "Confidence Intervals for Model Parameters",
      "topics": [
        "confint.dynrCook"
      ]
    },
    {
      "page": "diag-character-method",
      "title": "Create a diagonal matrix from a character vector",
      "topics": [
        "diag",
        "diag,character-method",
        "diag.character"
      ]
    },
    {
      "page": "dynr.config",
      "title": "Check that dynr in configured properly",
      "topics": [
        "dynr.config"
      ]
    },
    {
      "page": "dynr.cook",
      "title": "Cook a dynr model to estimate its free parameters",
      "topics": [
        "dynr.cook"
      ]
    },
    {
      "page": "dynr.data",
      "title": "Create a list of data for parameter estimation (cooking dynr) using 'dynr.cook'",
      "topics": [
        "dynr.data"
      ]
    },
    {
      "page": "dynr.flowField",
      "title": "A Function to plot the flow or velocity field for a one or two dimensional autonomous ODE system from the phaseR package written by Michael J. Grayling.",
      "topics": [
        "dynr.flowField"
      ]
    },
    {
      "page": "dynr.ggplot",
      "title": "The ggplot of the smoothed state estimates and the most likely regimes",
      "topics": [
        "autoplot.dynrCook",
        "dynr.ggplot"
      ]
    },
    {
      "page": "dynr.ldl",
      "title": "LDL Decomposition for Matrices",
      "topics": [
        "dynr.ldl"
      ]
    },
    {
      "page": "dynr.mi",
      "title": "Multiple Imputation of dynrModel objects",
      "topics": [
        "dynr.mi"
      ]
    },
    {
      "page": "dynr.model",
      "title": "Create a dynrModel object for parameter estimation (cooking dynr) using 'dynr.cook'",
      "topics": [
        "dynr.model"
      ]
    },
    {
      "page": "dynr.plotFreq",
      "title": "Plot of the estimated frequencies of the regimes across all individuals and time points based on their smoothed regime probabilities",
      "topics": [
        "dynr.plotFreq"
      ]
    },
    {
      "page": "dynr.taste",
      "title": "Detect outliers in state space models.",
      "topics": [
        "dynr.taste"
      ]
    },
    {
      "page": "dynr.taste2",
      "title": "Re-fit state-space model using the estimated outliers.",
      "topics": [
        "dynr.taste2"
      ]
    },
    {
      "page": "dynr.trajectory",
      "title": "A Function to perform numerical integration of the chosen ODE system, for a user-specified set of initial conditions. Plots the resulting solution(s) in the phase plane. This function from the phaseR package written by Michael J. Grayling.",
      "topics": [
        "dynr.trajectory"
      ]
    },
    {
      "page": "dynr.version",
      "title": "Current Version String",
      "topics": [
        "dynr.version"
      ]
    },
    {
      "page": "dynrCook-class",
      "title": "The dynrCook Class",
      "topics": [
        "$,dynrCook-method",
        "dynrCook-class",
        "dynrDebug-class",
        "print,dynrCook-method",
        "show,dynrCook-method"
      ]
    },
    {
      "page": "dynrDynamics-class",
      "title": "The dynrDynamics Class",
      "topics": [
        "dynrDynamics-class",
        "dynrDynamicsFormula-class",
        "dynrDynamicsMatrix-class"
      ]
    },
    {
      "page": "dynrInitial-class",
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