DESCRIPTION

Package: BayesPprobit
Type: Package
Title: Bayesian p-Generalized Probit Regression
Version: 1.0.0
Authors@R: c(
    person(given = "Zeyu", family = "Ding", email = "zeyu.ding@tu-dortmund.de", role = c("aut", "cre")), person(given = "Katja", family = "Ickstadt", email = "ickstadt@statistik.tu-dortmund.de", role = "aut"), person(given = "Simon", family = "Omlor", email = "simon.omlor@tu-dortmund.de", role = "aut"),person(given = "Alexander", family = "Munteanu", email = "alexander.munteanu@tu-dortmund.de", role = "aut"))
Description: Implements Bayesian p-generalized probit regression with flexible modeling 
    of binary outcomes using p-generalized Gaussian distribution as the link function. 
    The package provides efficient Markov Chain Monte Carlo (MCMC) sampling through 
    Metropolis-Hastings within Gibbs sampling, and enables scalable computation via 
    coreset construction methods. Key features include multiple chain sampling, 
    convergence diagnostics, and visualization tools. Methods are based on 
    Ding et al. (2024) <doi:10.1007/s11634-024-00599-1> "Scalable Bayesian 
    p-generalized probit and logistic regression".
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
Depends:
    R (>= 3.6.0)
Imports:
    stats,
    graphics,
    methods,
    sirt,
    utils,
    mvtnorm,
    Matrix,
    gnorm,
    coda
Suggests:
    testthat(>= 3.0.0),
    knitr,
    rmarkdown
URL: https://github.com/zeyudsai/BayesPprobit, https://link.springer.com/article/10.1007/s11634-024-00599-1
BugReports: https://github.com/zeyudsai/BayesPprobit/issues
Config/testthat/edition: 3
VignetteBuilder: knitr