RprobitB: Bayesian Probit Choice Modeling

Fits Bayesian probit models for binary, multinomial, ordered, and ranked choices in cross-sectional and panel data. Correlated or uncorrelated normal and log-normal random coefficients, finite mixtures, sparse finite mixtures, and Dirichlet process mixtures describe preference heterogeneity. Multiple Gibbs chains produce posterior draws for diagnostics and choice prediction. Empirical model data can be supplied as a data frame or simulated from the requested specification. For an overarching treatment of the methodology, see Oelschlaeger (2026) <https://pub.uni-bielefeld.de/record/3014719>. The latent-class model is described in Oelschlaeger and Bauer (2021) <https://trid.trb.org/view/1759753>.

Package overview README.md Bayesian model evaluation Get started with RprobitB Modeling preference heterogeneity Model specification and variants Posterior prediction

Vignettes Man pages API and functions Files

Package details
Author	Lennart Oelschläger [aut, cre] (ORCID: <https://orcid.org/0000-0001-5421-9313>), Dietmar Bauer [ctb] (ORCID: <https://orcid.org/0000-0003-2920-7032>)
Maintainer	Lennart Oelschläger <oelschlaeger.lennart@gmail.com>
License	GPL-3
Version	2.0.0
URL	https://loelschlaeger.de/RprobitB/ https://github.com/loelschlaeger/RprobitB
Package repository	View on CRAN
Installation	Install the latest version of this package by entering the following in R: `install.packages("RprobitB")`