vbpm - Variational Bayes Psychometric Models
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.
