R/bqror.R

In bqror: Bayesian Quantile Regression for Ordinal Models

#' Bayesian quantile regression for ordinal models
#'
#' @description
#'
#' Package provides functions for estimation and inference in Bayesian quantile regression with
#' ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by
#' a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model
#' with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm.
#' The summary output presents the posterior mean, posterior standard deviation, 95\% credible
#' intervals, and the inefficiency factors along with the two model comparison measures – logarithm of
#' marginal likelihood and the deviance information criterion (DIC). The package also provides
#' functions for computing the covariate effects and other functions that aids either the estimation
#' or inference in quantile ordinal models.
#'
#' @details
#' \deqn{Package: bqror}
#' \deqn{Type: Package}
#' \deqn{Version: 1.5.0}
#' \deqn{License: GPL (>=2)}
#'
#' Package \strong{bqror} provides the following functions:
#'
#' \itemize{
#' \item{For an ordinal model with three or more outcomes:}
#' }
#' \code{\link[bqror]{quantregOR1}}, \code{\link[bqror]{covEffectOR1}},
#' \code{\link[bqror]{logMargLikeOR1}}, \code{\link[bqror]{dicOR1}},
#' \code{\link[bqror]{qrnegLogLikensumOR1}}, \code{\link[bqror]{ineffactorOR1}},
#' \code{\link[bqror]{qrminfundtheorem}}, \code{\link[bqror]{drawbetaOR1}},
#' \code{\link[bqror]{drawwOR1}}, \code{\link[bqror]{drawlatentOR1}},
#' \code{\link[bqror]{drawdeltaOR1}}, \code{\link[bqror]{alcdfstd}},
#' \code{\link[bqror]{alcdf}}
#'
#' \itemize{
#' \item{For an ordinal model with three outcomes:}
#' }
#' \code{\link[bqror]{quantregOR2}}, \code{\link[bqror]{covEffectOR2}},
#' \code{\link[bqror]{logMargLikeOR2}}, \code{\link[bqror]{dicOR2}},
#' \code{\link[bqror]{qrnegLogLikeOR2}}, \code{\link[bqror]{ineffactorOR2}},
#' \code{\link[bqror]{drawlatentOR2}}, \code{\link[bqror]{drawbetaOR2}},
#' \code{\link[bqror]{drawsigmaOR2}}, \code{\link[bqror]{drawnuOR2}},
#' \code{\link[bqror]{rndald}}
#'
#' \itemize{
#' \item{Extractor Functions:}
#' }
#' \code{\link[bqror]{summary.bqrorOR1}}, \code{\link[bqror]{summary.bqrorOR2}}
#'
#' @author
#' Mohammad Arshad Rahman
#'
#' Prajual Maheshwari <prajual1391@gmail.com>
#'
#' @references
#' Rahman, M. A. (2016). “Bayesian
#' Quantile Regression for Ordinal Models.”
#' Bayesian Analysis, 11(1): 1-24. DOI: 10.1214/15-BA939
#'
#' Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and
#' Probability Letters, 54(4): 437–447. DOI: 10.1016/S0167-7152(01)00124-9
#'
#' Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica,
#' 46(1): 33-50. DOI: 10.2307/1913643
#'
#' Greenberg, E. (2012). “Introduction to Bayesian Econometrics.”
#' Cambridge University Press. Cambridge, DOI: 10.1017/CBO9781139058414
#'
#' @seealso \link[GIGrvg]{rgig}, \link[MASS]{mvrnorm}, \link[MASS]{ginv},
#' \link[truncnorm]{rtruncnorm}, \link[NPflow]{mvnpdf},
#' \link[invgamma]{rinvgamma}, \link[pracma]{mldivide},
#' \link[pracma]{rand}, \link[stats]{qnorm},
#' \link[stats]{rexp}, \link[stats]{rnorm},
#' \link[pracma]{std}, \link[stats]{sd}, \link[stats]{acf},
#' \link[pracma]{Reshape}, \link[progress]{progress_bar},
#' \link[invgamma]{dinvgamma}, \link[stats]{logLik}
#'
#' @docType package
#' @name bqror
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