{
  "_id": "6a104c79acfb0bcc41ca02f7",
  "Package": "bqror",
  "Type": "Package",
  "Title": "Bayesian Quantile Regression for Ordinal Models",
  "Version": "1.4.0",
  "URL": "https://github.com/prajual/bqror",
  "Authors@R": "c(person(\"Mohammad Arshad Rahman\", \"Developer\", role = \"aut\",\nemail = c(\"arshadrahman25@gmail.com\",\"marshad@iitk.ac.in\")),\nperson(\"Prajual Maheshwari\", role = \"cre\", email = \"prajual1391@gmail.com\"))",
  "Maintainer": "Prajual Maheshwari <prajual1391@gmail.com>",
  "Description": "Package provides functions for estimating Bayesian\nquantile regression with ordinal outcomes, computing the\ncovariate effects, model comparison measures, and inefficiency\nfactor. The generic ordinal model with 3 or more outcomes\n(labeled OR1 model) is estimated by a combination of Gibbs\nsampling and Metropolis-Hastings algorithm. Whereas an ordinal\nmodel with exactly 3 outcomes (labeled OR2 model) is estimated\nusing Gibbs sampling only. For each model framework, there is a\nspecific function for estimation. The summary output produces\nestimates for regression quantiles and two measures of model\ncomparison — log of marginal likelihood and Deviance\nInformation Criterion (DIC). The package also has specific\nfunctions for computing the covariate effects and other\nfunctions that aids either the estimation or inference in\nquantile ordinal models. Rahman, M. A. (2016).“Bayesian\nQuantile Regression for Ordinal Models.” Bayesian Analysis,\nII(I): 1-24 <doi: 10.1214/15-BA939>. Yu, K., and Moyeed, R. A.\n(2001). “Bayesian Quantile Regression.” Statistics and\nProbability Letters, 54(4): 437–447 <doi:\n10.1016/S0167-7152(01)00124-9>. Koenker, R., and Bassett, G.\n(1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 <doi:\n10.2307/1913643>. Chib, S. (1995). “Marginal likelihood from\nthe Gibbs output.” Journal of the American Statistical\nAssociation, 90(432):1313–1321, 1995. <doi:\n10.1080/01621459.1995.10476635>. Chib, S., and Jeliazkov, I.\n(2001). “Marginal likelihood from the Metropolis-Hastings\noutput.” Journal of the American Statistical Association,\n96(453):270–281, 2001. <doi: 10.1198/016214501750332848>.",
  "License": "GPL (>=2)",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "RoxygenNote": "7.1.2",
  "Config/testthat/edition": "3",
  "Config/pak/sysreqs": "libicu-dev libssl-dev",
  "Repository": "https://prajual.r-universe.dev",
  "Date/Publication": "2022-07-06 12:51:09 UTC",
  "RemoteUrl": "https://github.com/prajual/bqror",
  "RemoteRef": "HEAD",
  "RemoteSha": "be425e5060d75efeaebf47161d3803ca8a27698b",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-10 07:24:45 UTC",
    "User": "root"
  },
  "Author": "Mohammad Arshad Rahman Developer [aut],\nPrajual Maheshwari [cre]",
  "MD5sum": "6155bfeaa6103e50ac329c1abfd79fef",
  "_user": "prajual",
  "_type": "src",
  "_file": "bqror_1.4.0.tar.gz",
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  "_created": "2026-05-10T07:24:45.000Z",
  "_published": "2026-05-22T12:30:49.529Z",
  "_distro": "noble",
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    "author": "Prajual <Prajual1391@gmail.com>",
    "committer": "Prajual <Prajual1391@gmail.com>",
    "message": "Updated Package Code\n",
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    "name": "Prajual Maheshwari",
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  "_tags": [],
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    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
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  "_homeurl": "https://github.com/prajual/bqror",
  "_realowner": "prajual",
  "_cranurl": true,
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      "date": "2020-02-26"
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    "alcdf",
    "alcdfstd",
    "covEffectOR1",
    "covEffectOR2",
    "devianceOR1",
    "devianceOR2",
    "drawbetaOR1",
    "drawbetaOR2",
    "drawdeltaOR1",
    "drawlatentOR1",
    "drawlatentOR2",
    "drawnuOR2",
    "drawsigmaOR2",
    "drawwOR1",
    "infactorOR1",
    "infactorOR2",
    "logMargLikeOR1",
    "logMargLikeOR2",
    "qrminfundtheorem",
    "qrnegLogLikensumOR1",
    "qrnegLogLikeOR2",
    "quantregOR1",
    "quantregOR2",
    "rndald"
  ],
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    {
      "name": "data25j3",
      "title": "Simulated data from OR2 model for p = 0.25 (i.e., 25th quantile)",
      "object": "data25j3",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "data25j4",
      "title": "Simulated data from OR1 model for p = 0.25 (i.e., 25th quantile)",
      "object": "data25j4",
      "class": [
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      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "data50j3",
      "title": "Simulated data from OR2 model for p = 0.5 (i.e., 50th quantile)",
      "object": "data50j3",
      "class": [
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      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "data50j4",
      "title": "Simulated data from OR1 model for p = 0.5 (i.e., 50th quantile)",
      "object": "data50j4",
      "class": [
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      "fields": [],
      "table": false,
      "tojson": true
    },
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      "title": "Simulated data from OR2 model for p = 0.75 (i.e., 75th quantile)",
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      "fields": [],
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      "tojson": true
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      "title": "Simulated data from OR1 model for p = 0.75 (i.e., 75th quantile)",
      "object": "data75j4",
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      "fields": [],
      "table": false,
      "tojson": true
    },
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      "name": "Educational_Attainment",
      "title": "Educational Attainment study based on data from the National Longitudinal Study of Youth (NLSY, 1979) survey.",
      "object": "Educational_Attainment",
      "class": [
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      ],
      "fields": [
        "dep_edu_level",
        "mother_work",
        "urban",
        "south",
        "father_educ",
        "mother_educ",
        "fam_income",
        "female",
        "black",
        "age_cohort_2",
        "age_cohort_3",
        "age_cohort_4"
      ],
      "rows": 3923,
      "table": true,
      "tojson": true
    },
    {
      "name": "Policy_Opinion",
      "title": "Data contains public opinion on the proposal to raise federal income taxes for couples (individuals) earning more than $250,000 ($200,000) per year and a host of other covariates. The data is taken from the 2010-2012 American National Election Studies (ANES) on the Evaluation of Government and Society Study I (EGSS 1)",
      "object": "Policy_Opinion",
      "class": [
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      "fields": [
        "y",
        "Intercept",
        "AgeCat",
        "IncomeCat",
        "Bachelors",
        "Post.Bachelors",
        "Computers",
        "CellPhone",
        "White"
      ],
      "rows": 1164,
      "table": true,
      "tojson": true
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  ],
  "_help": [
    {
      "page": "alcdf",
      "title": "cdf of an asymmetric Laplace distribution",
      "topics": [
        "alcdf"
      ]
    },
    {
      "page": "alcdfstd",
      "title": "cdf of a standard asymmetric Laplace distribution",
      "topics": [
        "alcdfstd"
      ]
    },
    {
      "page": "bqror",
      "title": "Bayesian quantile regression for ordinal models",
      "topics": [
        "bqror"
      ]
    },
    {
      "page": "covEffectOR1",
      "title": "Covariate effect for OR1 model",
      "topics": [
        "covEffectOR1"
      ]
    },
    {
      "page": "covEffectOR2",
      "title": "Covariate effect for OR2 model",
      "topics": [
        "covEffectOR2"
      ]
    },
    {
      "page": "data25j3",
      "title": "Simulated data from OR2 model for p = 0.25 (i.e., 25th quantile)",
      "topics": [
        "data25j3"
      ]
    },
    {
      "page": "data25j4",
      "title": "Simulated data from OR1 model for p = 0.25 (i.e., 25th quantile)",
      "topics": [
        "data25j4"
      ]
    },
    {
      "page": "data50j3",
      "title": "Simulated data from OR2 model for p = 0.5 (i.e., 50th quantile)",
      "topics": [
        "data50j3"
      ]
    },
    {
      "page": "data50j4",
      "title": "Simulated data from OR1 model for p = 0.5 (i.e., 50th quantile)",
      "topics": [
        "data50j4"
      ]
    },
    {
      "page": "data75j3",
      "title": "Simulated data from OR2 model for p = 0.75 (i.e., 75th quantile)",
      "topics": [
        "data75j3"
      ]
    },
    {
      "page": "data75j4",
      "title": "Simulated data from OR1 model for p = 0.75 (i.e., 75th quantile)",
      "topics": [
        "data75j4"
      ]
    },
    {
      "page": "devianceOR1",
      "title": "Deviance Information Criterion for OR1 model",
      "topics": [
        "devianceOR1"
      ]
    },
    {
      "page": "devianceOR2",
      "title": "Deviance Information Criterion for OR2 model",
      "topics": [
        "devianceOR2"
      ]
    },
    {
      "page": "drawbetaOR1",
      "title": "Samples beta for OR1 model",
      "topics": [
        "drawbetaOR1"
      ]
    },
    {
      "page": "drawbetaOR2",
      "title": "Samples beta for model OR2",
      "topics": [
        "drawbetaOR2"
      ]
    },
    {
      "page": "drawdeltaOR1",
      "title": "Samples delta for OR1 model",
      "topics": [
        "drawdeltaOR1"
      ]
    },
    {
      "page": "drawlatentOR1",
      "title": "Samples latent variable z for OR1 model",
      "topics": [
        "drawlatentOR1"
      ]
    },
    {
      "page": "drawlatentOR2",
      "title": "Samples latent variable z for OR2 model",
      "topics": [
        "drawlatentOR2"
      ]
    },
    {
      "page": "drawnuOR2",
      "title": "Samples scale factor nu for OR2 model",
      "topics": [
        "drawnuOR2"
      ]
    },
    {
      "page": "drawsigmaOR2",
      "title": "Samples sigma for OR2 model",
      "topics": [
        "drawsigmaOR2"
      ]
    },
    {
      "page": "drawwOR1",
      "title": "Samples latent weight w for OR1 model",
      "topics": [
        "drawwOR1"
      ]
    },
    {
      "page": "Educational_Attainment",
      "title": "Educational Attainment study based on data from the National Longitudinal Study of Youth (NLSY, 1979) survey.",
      "topics": [
        "Educational_Attainment"
      ]
    },
    {
      "page": "infactorOR1",
      "title": "Inefficiency factor for OR1 model",
      "topics": [
        "infactorOR1"
      ]
    },
    {
      "page": "infactorOR2",
      "title": "Inefficiency factor for OR2 model",
      "topics": [
        "infactorOR2"
      ]
    },
    {
      "page": "logLik.bqrorOR1",
      "title": "Extractor function for log marginal likelihood for OR1 model",
      "topics": [
        "logLik",
        "logLik.bqrorOR1"
      ]
    },
    {
      "page": "logMargLikeOR1",
      "title": "Logarithm marginal likelihood for OR1 model",
      "topics": [
        "logMargLikeOR1"
      ]
    },
    {
      "page": "logMargLikeOR2",
      "title": "Marginal likelihood for OR2 model",
      "topics": [
        "logMargLikeOR2"
      ]
    },
    {
      "page": "Policy_Opinion",
      "title": "Data contains public opinion on the proposal to raise federal income taxes for couples (individuals) earning more than $250,000 ($200,000) per year and a host of other covariates. The data is taken from the 2010-2012 American National Election Studies (ANES) on the Evaluation of Government and Society Study I (EGSS 1)",
      "topics": [
        "Policy_Opinion"
      ]
    },
    {
      "page": "qrminfundtheorem",
      "title": "Minimizes the negative of log-likelihood for OR1 model",
      "topics": [
        "qrminfundtheorem"
      ]
    },
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      "page": "qrnegLogLikensumOR1",
      "title": "Negative log-likelihood for OR1 model",
      "topics": [
        "qrnegLogLikensumOR1"
      ]
    },
    {
      "page": "qrnegLogLikeOR2",
      "title": "Negative sum of log-likelihood for OR2 model",
      "topics": [
        "qrnegLogLikeOR2"
      ]
    },
    {
      "page": "quantregOR1",
      "title": "Bayesian quantile regression for OR1 model",
      "topics": [
        "quantregOR1"
      ]
    },
    {
      "page": "quantregOR2",
      "title": "Bayesian quantile regression for OR2 model",
      "topics": [
        "quantregOR2"
      ]
    },
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      "page": "rndald",
      "title": "Generates random numbers from an AL distribution",
      "topics": [
        "rndald"
      ]
    }
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