% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vbpm-package.R
\docType{package}
\name{vbpm-package}
\alias{vbpm}
\alias{vbpm-package}
\title{vbpm: Variational Bayes Psychometric Models}
\description{
Variational Bayes estimation for a family of psychometric measurement models. Two models are provided. Variational Bayes factor analysis (vbfa) is a regularized partially confirmatory factor model spanning the confirmatory-exploratory continuum via spike-and-slab priors on the loadings (Chen, Guo, Zhang, and Pan, 2021 \doi{10.1037/met0000293}; Chen, 2023 \doi{10.3758/s13428-022-01884-7}; Jin and Chen, 2025 \doi{10.1080/10705511.2024.2432612}), with an optional dynamic (warm-started) regularization path, an orthogonal bifactor parameterization, and optional sparse residual (local dependence) estimation through a graphical spike-and-slab prior solved by QUIC (Jin, Chen, Yan, and Zhang, 2026 \doi{10.31234/osf.io/dehtv_v2}). Regularized MIMIC (vbmimic) extends this to multiple-indicators multiple-causes models, placing spike-and-slab priors on both the measurement and the structural part (Jin and Chen, 2025 \doi{10.1080/00273171.2025.2483253}). Companion tools compute SEM-like fit statistics, and sweep a factor-count window to report candidate fit, criterion, and between-candidate loading-correspondence measurements without selecting a count (Chen and Jin, 2026 \doi{10.48550/arXiv.2607.07159}). Data generators for either model family are also provided.
}
\seealso{
Useful links:
\itemize{
  \item \url{https://github.com/Jinsong-Chen/vbpm}
  \item Report bugs at \url{https://github.com/Jinsong-Chen/vbpm/issues}
}

}
\author{
\strong{Maintainer}: Jinsong Chen \email{jinsong.chen@live.com} (\href{https://orcid.org/0000-0002-0157-5469}{ORCID})

Authors:
\itemize{
  \item Jinsong Chen \email{jinsong.chen@live.com} (\href{https://orcid.org/0000-0002-0157-5469}{ORCID})
  \item Yi Jin (\href{https://orcid.org/0000-0002-8604-5992}{ORCID})
}

}
\keyword{internal}
