vbpm

Variational Bayes Psychometric Models

vbpm is a home for variational Bayes estimation of psychometric measurement models. Its models are partially confirmatory: they span the confirmatory-to-exploratory continuum, with parameters on specified paths anchored by a design matrix and parameters on unspecified paths selected from the data by continuous spike-and-slab priors.

The package provides variational Bayes factor analysis (vbfa), where a design matrix Q governs the item loadings, and regularized MIMIC (vbmimic), which adds covariates predicting the factors and regularizes both the measurement part (Q_A) and the structural part (Q_B).

Features

• vbfa() — the factor-analysis estimator.
  • Spike-and-slab regularization of unspecified loadings.
  • Dynamic exploration: pass v0 a decreasing vector for a warm-started regularization path (Ročková & George, 2018; Jin, Chen, Yan, & Zhang, 2026); a scalar v0 is a single fixed spike. v0 = 0.001 reproduces the fixed-spike estimator exactly.
  • Bifactor: bifactor = TRUE fits one general factor plus K orthogonal group factors, with Q supplying only the group design – so K counts group factors, comparable to an oblique K-factor model. The general argument designs the general column (including bifactor-(S-1)-style zeros). orthogonal = TRUE remains available for plain orthogonal models (or hand-built bifactor designs).
  • Local dependence: set ld = TRUE to estimate a sparse residual precision by a graphical spike-and-slab prior (QUIC) — fully exploratory by default, or restricted by a residual design matrix Qe (Jin, Chen, Yan, & Zhang, 2026).
• vbmimic() — the MIMIC estimator.
  • Spike-and-slab regularization of both the measurement design (Q_A, items on factors) and the structural design (Q_B, factors on covariates).
  • Supports the same warm-started v0 path. The defaults reproduce the published estimator exactly.
  • The ELBO is not yet available for this model, so fit_stats() returns a clearly marked limited result and pefa() sweeps vbfa() candidates only.
• fit_stats() — SEM-like fit statistics (RMSEA, SRMR, CFI, TLI, AIC, BIC) plus the model’s objective. An S3 generic over fitted models: it builds the implied covariance from the residual covariance, so it is correct for diagonal and local-dependence vbfa fits, and it reads orthogonal/ld from the fit rather than asking you to repeat them. Information criteria use the nominal hard-selected parameter count by default; the numerical Jacobian-rank count is an explicit rank_adjust = TRUE sensitivity.
• special_effects() — post-process a bifactor fit: Schmid-Leiman proportionality CVs, approximate higher-order parameters (second-order loadings per group factor), and testlet/special effect sizes (Zhang & Chen, 2024, Eq. 16).
• pefa() — fit one partially exploratory model for every K in a fixed consecutive window and report what was measured. It selects no factor count and carries no cut, threshold, persistence horizon, collision policy, or stopping rule; composing the numbers into a conclusion is yours. Three tables:
  • $sweep — one row per candidate: K, ELBO, AIC, BIC, RMSEA, SRMR, CFI, TLI, t, iter, converged.
  • $transitions — one row per adjacent pair: percent-of-largest-positive ELBO and BIC gains, plus the seven structural facts phi_min, rmsd, rmsd_max, ari, pip_rmsd, unmatched_ssl, collision.
  • $persistence — three upper-triangular matrices, phi (holding phi_min), rmsd (holding rmsd_max), and a logical collision mask, covering every ordered pair. Read them directly as x$persistence$phi; there is no accessor, because selecting a stored triangle would only be a synonym for $.
  Columns correspond by minimum sign-aligned squared distance: each source column takes the target minimizing the squared length of a - s b, with s = 1 when a'b >= 0 and -1 otherwise — Equation 17 of Chen (2023) made invariant to whole-column reflection. Backbone columns pair by position, each exploratory column then chooses independently, and congruence only breaks exact ties. The assignment is deliberately not one-to-one: when two columns choose the same target that reuse is a collision, reported as evidence and never repaired into a forced matching. Every ordered pair is compared directly from its two endpoints, never chained through the candidates between them. Candidate matrices live in K-named $loadings and $pips lists (result$loadings[["4"]]). Convergence is recorded, never used to suppress a number that is defined: a candidate that stopped at max_it keeps its matrices and all of its comparisons.
• ssl() — per-candidate sums of squared loadings, colSums(Lam^2), on the same scale as unmatched_ssl. It screens nothing and enters no correspondence rule; it is what distinguishes a low phi_min caused by a near-empty column from one caused by structure actually moving. Compact print(), summary(), and plot() methods complete the set; none of them announces a selected count, because none is chosen.
• sim_fa() and sim_lvm() — two complementary data generators. sim_fa() is driven by the loading pattern (items per factor, alternating cross-loadings, minor factors) and can generate higher-order/testlet data directly (gamma = second-order loadings, via the Schmid-Leiman equivalence); sim_lvm() is driven by a loading matrix and covers predictor designs (observed and latent) and mixed response formats. Both simulate local dependence, categorical items, and missingness.

Installation

1. A C++ toolchain (one-time setup)

vbpm ships C++ code — the QUIC solver behind the local-dependence branch — so installing it compiles that code and a C++ toolchain must be present. Many R users already have one (any package installed from source needs it); check before installing anything:

# install.packages("pkgbuild")
pkgbuild::has_build_tools(debug = TRUE)   # TRUE = you are ready, skip to step 2

If that returns FALSE:

Restart R afterwards, then re-run the has_build_tools() check.

2. The package

# install.packages("remotes")
remotes::install_github("Jinsong-Chen/vbpm", build_vignettes = TRUE)

Or clone and install from source:

git clone https://github.com/Jinsong-Chen/vbpm.git
R CMD INSTALL vbpm

Then:

library(vbpm)
vignette("vbfa")         # start here
vignette("pefa")         # the factor-count sweep and its three tables
vignette("bifactor")     # bifactor structures, AO/AZ backbones, and sweeps
vignette("vbmimic")      # covariates predicting the factors
citation("vbpm")

Building the vignettes needs knitr, rmarkdown, and pandoc (bundled with RStudio; from a plain shell, point RSTUDIO_PANDOC at it).

Quick start

library(vbpm)

sim <- sim_fa(N = 500, K = 3, ipf = 6, lam = .7, lac = .3)
Y   <- sim$dat

## partially confirmatory Q: 1 = anchor, 0 = fixed zero, -1 = estimate
Q <- matrix(-1, ncol(Y), 3)
Q[1:2, ] <- 0; Q[1:2, 1] <- 1        # two anchors on factor 1 only

fit <- vbfa(Y, Q)                    # dynamic path on by default; quiet
fit                                  # compact summary (S3 print method)
idx <- fit_stats(fit)                # nominal hard-selected count by default

Fits are ordinary named lists with a shared vbpm_fit class attached — fields such as fit$Lam, fit$pi, and fit$Phi remain public API. Focused vignettes cover vbfa, one pefa() sweep walked table by table under overextraction, bifactor/higher-order/testlet structures with AO and AZ backbones, and vbmimic.

MIMIC, with covariates predicting the factors:

B <- matrix(0, 3, 9)                 # sparse structural design: 3 covariates
for (k in 1:3) B[k, (k*3-2):(k*3)] <- .3   # per factor

sim <- sim_lvm(N = 500, K = 3, J = 18, P = 9, b = B, phx = 0)
Y   <- sim$dat[, 1:18]               # items first ...
X   <- sim$dat[, 19:27]              # ... covariates last

## anchor two items per factor; leave the structural part exploratory.
## (With BOTH parts fully exploratory the solution can be rotationally
## ambiguous -- anchor at least one part.)
Q_A <- matrix(-1L, 18, 3)
for (k in 1:3) { a <- which(rep(1:3, each = 6) == k)[1:2]
                 Q_A[a, ] <- 0L; Q_A[a, k] <- 1L }
Q_B <- matrix(-1L, 3, 9)

fit <- vbmimic(Y, X, Q_A, Q_B)
round(fit$B, 2)                      # which covariates predict which factors

References

• Partially confirmatory factor analysis with the Bayesian Lasso: Chen, J., Guo, Z., Zhang, L., & Pan, J. (2021). A partially confirmatory approach to scale development with the Bayesian Lasso. Psychological Methods, 26(2), 210–235. https://doi.org/10.1037/met0000293
• Generalized PCFA (mixed response formats; missing data under MAR): Chen, J. (2021). A generalized partially confirmatory factor analysis framework with mixed Bayesian Lasso methods. Multivariate Behavioral Research, 57(6), 879–894. https://doi.org/10.1080/00273171.2021.1925520
• Bifactor, higher-order, and testlet effects within GPCFA (including effect sizes for testlet effects): Zhang, Y., & Chen, J. (2024). Accommodating and extending various models for special effects within the generalized partially confirmatory factor analysis framework. Applied Psychological Measurement, 48(4–5), 208–229. https://doi.org/10.1177/01466216241261704
• Fully and partially exploratory factor analysis: Chen, J. (2023). Fully and partially exploratory factor analysis with bi-level Bayesian regularization. Behavior Research Methods, 55(4), 2125–2142. https://doi.org/10.3758/s13428-022-01884-7
• Variational estimator: Jin, Y., & Chen, J. (2025). Regularized variational approximation for partially confirmatory factor analysis. Structural Equation Modeling, 32(3), 437–449. https://doi.org/10.1080/10705511.2024.2432612
• Warm-started regularization path and sparse residual/local-dependence estimation: Jin, Y., Chen, J., Yan, Z., & Zhang, Y. (2026). Sparse residual estimation in partially confirmatory factor analysis. PsyArXiv preprint. https://doi.org/10.31234/osf.io/dehtv_v2
• MIMIC: Jin, Y., & Chen, J. (2025). Regularized variational Bayesian approximations for variable selection in extended multiple-indicators multiple-causes models. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2025.2483253
• Fit assessment and factor-number selection: Chen, J., & Jin, Y. (2026). Recovering latent structures after variational Bayesian variable selection: Fit assessment and factor-number selection in partially exploratory factor analysis. arXiv preprint arXiv:2607.07159. https://arxiv.org/abs/2607.07159

Known limitations

As of 0.9.1:

• Response types. The estimators model continuous Gaussian responses, and in-loop missing-data support covers that case in both vbfa() and vbmimic(). Categorical and mixed responses are out of scope for the current line of releases — supporting them means adding a threshold model and augmenting latent responses, which is a modelling extension rather than a feature. Simulating them is supported (cati/noc in sim_fa(), ilvl in sim_lvm()); estimating from them is not.
• Bifactor pefa() sweeps are a research extension. Their K counts group factors, and every comparison removes the labelled general column before applying exactly the loading/PIP correspondence an ordinary oblique sweep uses. General and group columns can nevertheless redistribute common variance as K changes, so a study that needs both modes should run them on the same data, backbone, and window rather than assume that either route must agree. The bifactor vignette shows one such pair of sweeps, where the bifactor criterion path and persistence triangle separate the candidates far less than the ordinary ones do. That is a property of that example, not a general ranking of the two modes.
• No calibrated thresholds. Nothing in the package tells you how large a congruence, how small an RMSD, or how steep a gain has to be. The vignettes show readings composed in local code and label them as such; none of those cuts has been validated against an error rate here.
• No DIF paths in vbmimic(). Covariates predict the factors (Q_B, i.e. impact); there are no direct covariate-to-item paths, so the model cannot currently express or detect differential item functioning.
• Complete covariates. vbmimic() requires complete X: covariates are conditioned on rather than modelled, so the model supplies no distribution to impute them from.
• LD objective. Local-dependence vbfa() fits return the terminal VECM objective and LD-aware fit statistics. ELBO is intentionally NA: calling the point-updated precision objective a joint variational lower bound would be misleading.
• ELBO for MIMIC. vbmimic() converges on a residual criterion rather than the bound, so ELBO is NA. fit_stats() describes the available parameter counts but covariance fit indices await a fully specified joint covariance.
• Fully exploratory MIMIC. With both Q_A and Q_B left entirely unspecified the model converges cleanly but the solution can be rotationally ambiguous. Anchor at least one part.

vbfa() reproduces its published reference output. A stored-reference regression test checks that result during every R CMD check, at a strict relative tolerance of 1e-8 that remains sensitive to material regression. It is not asserted bitwise: the estimator runs through compiled BLAS/LAPACK, whose SIMD and FMA choices differ between platforms, so the last one or two ulps are a property of the build rather than of the estimator.

See NEWS.md for the per-version changelog.

License

GPL-3.

Authors

• Jinsong Chen (maintainer) — @Jinsong-Chen
• Yi Jin — @jinyi1998