{
  "_id": "6ad33ce462c1581dfb2faea4",
  "Package": "BEND",
  "Title": "Bayesian Estimation of Nonlinear Data (BEND)",
  "Version": "2.1.2",
  "Authors@R": "c(\nperson(\"Corissa T.\", \"Rohloff\", email = \"corissa.wurth@gmail.com\", role = c(\"aut\",\"cre\"), comment = c(ORCID = \"0000-0003-3228-4653\")),\nperson(\"Rik\", \"Lamm\", email = \"rzlamm@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0002-3317-6243\")),\nperson(\"Yadira\", \"Peralta\", email = \"yadira.peralta@cide.edu\", role = \"aut\", comment = c(ORCID = \"0000-0003-4823-6939\")),\nperson(\"Nidhi\", \"Kohli\", email = \"nkohli@umn.edu\", role = \"aut\", comment = c(ORCID = \"0000-0003-4690-2854\")),\nperson(\"Eric F.\", \"Lock\", email = \"elock@umn.edu\", role = \"aut\", comment = c(ORCID = \"0000-0003-4663-2356\")))",
  "Description": "Provides a set of models to estimate nonlinear\nlongitudinal data using Bayesian estimation methods. These\nmodels include the: 1) Bayesian Piecewise Random Effects Model\n(Bayes_PREM()) which estimates a piecewise random effects\n(mixture) model for a given number of latent classes and a\nlatent number of possible changepoints in each class, and can\nincorporate class and outcome predictive covariates (see Lamm\n(2022) <https://hdl.handle.net/11299/252533> and Lock et al.,\n(2018) <doi:10.1007/s11336-017-9594-5>), 2) Bayesian Crossed\nRandom Effects Model (Bayes_CREM()) which estimates a linear,\nquadratic, exponential, or piecewise crossed random effects\nmodels where individuals are changing groups over time (e.g.,\nstudents and schools; see Rohloff et al., (2024)\n<doi:10.1111/bmsp.12334>), and 3) Bayesian Bivariate Piecewise\nRandom Effects Model (Bayes_BPREM()) which estimates a\nbivariate piecewise random effects model to jointly model two\nrelated outcomes (e.g., reading and math achievement; see\nPeralta et al., (2022) <doi:10.1037/met0000358>).",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "RoxygenNote": "8.0.0",
  "URL": "https://github.com/crohlo/BEND",
  "BugReports": "https://github.com/crohlo/BEND/issues",
  "LazyData": "true",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-10-17 09:13:55 UTC",
    "User": "root"
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  "Author": "Corissa T. Rohloff [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-3228-4653>), Rik Lamm [aut]\n(ORCID: <https://orcid.org/0000-0002-3317-6243>), Yadira\nPeralta [aut] (ORCID: <https://orcid.org/0000-0003-4823-6939>),\nNidhi Kohli [aut] (ORCID:\n<https://orcid.org/0000-0003-4690-2854>), Eric F. Lock [aut]\n(ORCID: <https://orcid.org/0000-0003-4663-2356>)",
  "Maintainer": "Corissa T. Rohloff <corissa.wurth@gmail.com>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-08-25 16:30:02 UTC",
  "RemoteUrl": "https://github.com/cran/BEND",
  "RemoteRef": "HEAD",
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  "_user": "cran",
  "_type": "src",
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  "_created": "2026-10-17T09:13:55.000Z",
  "_published": "2026-10-17T09:16:20.590Z",
  "_buildurl": "https://github.com/r-universe/cran/actions/runs/39020158889",
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    "description": "Unofficial read-only mirror of all CRAN R packages"
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/BEND"
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  "_devurl": "https://github.com/crohlo/bend",
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  "_topics": [
    "jags",
    "cpp"
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  "_rbuild": "4.6.1",
  "_assets": [
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    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/manual.pdf",
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    "extra/readme.html",
    "extra/readme.md",
    "LICENSE"
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  "_homeurl": "https://github.com/crohlo/bend",
  "_realowner": "crohlo",
  "_cranurl": false,
  "_releases": [
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      "version": "1.0",
      "date": "2024-03-23"
    },
    {
      "version": "1.1",
      "date": "2025-09-24"
    },
    {
      "version": "2.0.0",
      "date": "2026-03-31"
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      "date": "2026-04-19"
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  "_exports": [
    "Bayes_BPREM",
    "Bayes_CREM",
    "Bayes_PREM",
    "getClassProb",
    "getCoef",
    "getFitted",
    "getFixEf",
    "getKProb",
    "getModelFit",
    "getRanEf",
    "getVarCov",
    "plot_BEND"
  ],
  "_datasets": [
    {
      "name": "results_bprem",
      "title": "Fitted results for a BPREM",
      "object": "results_bprem",
      "class": [
        "BPREM",
        "BEND"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "results_pcrem",
      "title": "Fitted results for a PCREM",
      "object": "results_pcrem",
      "class": [
        "CREM",
        "BEND"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "results_prem",
      "title": "Fitted results for a PREM",
      "object": "results_prem",
      "class": [
        "PREM",
        "BEND"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "SimData_BPREM",
      "title": "Simulated data for a BPREM",
      "object": "SimData_BPREM",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "time",
        "y1",
        "y2"
      ],
      "rows": 210,
      "table": true,
      "tojson": true
    },
    {
      "name": "SimData_PCREM",
      "title": "Simulated data for a PCREM",
      "object": "SimData_PCREM",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "teacherid",
        "time",
        "y"
      ],
      "rows": 210,
      "table": true,
      "tojson": true
    },
    {
      "name": "SimData_PREM",
      "title": "Simulated data for a PREM + Extensions",
      "object": "SimData_PREM",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "time",
        "y",
        "class_pred_1",
        "class_pred_2",
        "outcome_pred_1"
      ],
      "rows": 540,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "Bayes_BPREM",
      "title": "Bayesian Bivariate Piecewise Random Effects Model (BPREM)",
      "topics": [
        "Bayes_BPREM"
      ]
    },
    {
      "page": "Bayes_CREM",
      "title": "Bayesian Crossed Random Effects Model (CREM)",
      "topics": [
        "Bayes_CREM"
      ]
    },
    {
      "page": "Bayes_PREM",
      "title": "Bayesian Piecewise Random Effects Model (PREM) + Extensions",
      "topics": [
        "Bayes_PREM"
      ]
    },
    {
      "page": "getClassProb",
      "title": "Extract class probabilities",
      "topics": [
        "getClassProb",
        "getClassProb.PREM",
        "print.getClassProb.PREM"
      ]
    },
    {
      "page": "getCoef",
      "title": "Extract random coefficients",
      "topics": [
        "getCoef",
        "getCoef.BEND",
        "print.getCoef"
      ]
    },
    {
      "page": "getFitted",
      "title": "Extract fitted values",
      "topics": [
        "getFitted",
        "getFitted.BEND",
        "print.getFitted"
      ]
    },
    {
      "page": "getFixEf",
      "title": "Extract fixed effects parameter estimates",
      "topics": [
        "getFixEf",
        "getFixEf.BPREM",
        "getFixEf.CREM",
        "getFixEf.PREM",
        "print.getFixEf.BEND"
      ]
    },
    {
      "page": "getKProb",
      "title": "Extract changepoint probabilities",
      "topics": [
        "getKProb",
        "getKProb.PREM",
        "print.getKProb.PREM"
      ]
    },
    {
      "page": "getModelFit",
      "title": "Extract model fit",
      "topics": [
        "getModelFit",
        "getModelFit.BEND",
        "print.getModelFit.BEND"
      ]
    },
    {
      "page": "getRanEf",
      "title": "Extract random effects",
      "topics": [
        "getRanEf",
        "getRanEf.BPREM",
        "getRanEf.CREM",
        "print.getRanEf.BPREM",
        "print.getRanEf.CREM"
      ]
    },
    {
      "page": "getVarCov",
      "title": "Extract random effects variance-covariance matrix",
      "topics": [
        "getVarCov",
        "getVarCov.BPREM",
        "getVarCov.CREM",
        "getVarCov.PREM",
        "print.getVarCov.BPREM",
        "print.getVarCov.CREM",
        "print.getVarCov.PREM"
      ]
    },
    {
      "page": "plot_BEND",
      "title": "Plot a BEND Model (PREM, CREM, BPREM)",
      "topics": [
        "plot_BEND"
      ]
    },
    {
      "page": "plot.BPREM",
      "title": "Plot the results of a bivariate piecewise random effects model (BPREM)",
      "topics": [
        "plot.BPREM"
      ]
    },
    {
      "page": "plot.CREM",
      "title": "Plot the results of a crossed random effects model (CREM)",
      "topics": [
        "plot.CREM"
      ]
    },
    {
      "page": "plot.PREM",
      "title": "Plot the results of a piecewise random effects model (PREM)",
      "topics": [
        "plot.PREM"
      ]
    },
    {
      "page": "print.BPREM",
      "title": "Print the results of a bivariate piecewise random effects model (BPREM)",
      "topics": [
        "print.BPREM"
      ]
    },
    {
      "page": "print.CREM",
      "title": "Print the results of a crossed random effects model (CREM)",
      "topics": [
        "print.CREM"
      ]
    },
    {
      "page": "print.PREM",
      "title": "Print the results of a piecewise random effects model (PREM)",
      "topics": [
        "print.PREM"
      ]
    },
    {
      "page": "results_bprem",
      "title": "Fitted results for a BPREM",
      "topics": [
        "results_bprem"
      ]
    },
    {
      "page": "results_pcrem",
      "title": "Fitted results for a PCREM",
      "topics": [
        "results_pcrem"
      ]
    },
    {
      "page": "results_prem",
      "title": "Fitted results for a PREM",
      "topics": [
        "results_prem"
      ]
    },
    {
      "page": "SimData_BPREM",
      "title": "Simulated data for a BPREM",
      "topics": [
        "SimData_BPREM"
      ]
    },
    {
      "page": "SimData_PCREM",
      "title": "Simulated data for a PCREM",
      "topics": [
        "SimData_PCREM"
      ]
    },
    {
      "page": "SimData_PREM",
      "title": "Simulated data for a PREM + Extensions",
      "topics": [
        "SimData_PREM"
      ]
    },
    {
      "page": "summary.BPREM",
      "title": "Summarize the results of a bivariate piecewise random effects model (BPREM)",
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        "print.summary.BPREM",
        "summary.BPREM"
      ]
    },
    {
      "page": "summary.CREM",
      "title": "Summarize the results of a crossed random effects model (CREM)",
      "topics": [
        "print.summary.CREM",
        "summary.CREM"
      ]
    },
    {
      "page": "summary.PREM",
      "title": "Summarize the results of a piecewise random effects model (PREM)",
      "topics": [
        "print.summary.PREM",
        "summary.PREM"
      ]
    }
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