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Changelog

Source: NEWS.md

INLAvaan (development version)

INLAvaan 0.2.4

CRAN release: 2026-04-03

New features

  • bfit_indices() computes per-sample Bayesian fit index vectors (BRMSEA, BCFI, BTLI, BNFI), with summary() and print() methods. Summary statistics are also available via fitmeasures().
  • compare() compares two or more fitted models side by side, reporting marginal log-likelihood, Bayes factors, and DIC, with optional fit measures from fitmeasures().
  • diagnostics() computes global and per-parameter convergence and approximation-quality diagnostics for fitted models.
  • get_inlavaan_internal() is now exported and documented, providing access to the internal list stored in a fitted INLAvaan object.
  • predict() generates predictions for observed data and missing data imputation, respecting multilevel structure if present.
  • sampling() draws from the posterior (or prior) SEM generative model, returning parameter vectors, latent variables, or observed variables.
  • simulate() generates complete replicate datasets from a fitted model, useful for simulation-based calibration and posterior predictive checks.
  • timing() extracts wall-clock timings for individual computation stages of a fitted model.

Minor improvements and fixes

  • Cholesky factorisation of the precision matrix replaces raw solve() for covariance and log-determinant calculations.
  • Copula sampling with NORTA (NORmal To Anything) correlation adjustment is now the default (samp_copula = TRUE), ensuring posterior samples have correct skew-normal marginals and correct Pearson correlations.
  • Pre-computed Owen-scrambled Sobol sequences are used by default, with fallback to {qrng} for larger sequences. QMC sample size now scales with model dimension.
  • Skew-normal fitting now runs in parallel automatically when the number of marginals exceeds 120, using all available cores.
  • Small optimisations to the skew-normal volume correction.
  • acfa(), asem(), and agrowth() gain a vb_correction argument.
  • ggplot2 is now optional; plots fall back to base R graphics when it is not installed.
  • inlavaan() gains an sn_fit_ngrid argument to control the number of grid points per dimension when fitting skew-normal marginals (default 21).
  • inlavaan() now supports sn_fit_sample = TRUE for defined parameters, fitting a skew-normal approximation to their posterior marginals based on drawn samples.
  • plot() method gains improved visualisation options.
  • priors_for() now supports the [prec] scale qualifier for variance parameters (theta, psi), placing the prior on the precision scale with automatic Jacobian adjustment.
  • sampling() and simulate() gain a silent argument to suppress informational messages.
  • summary() now includes 25th and 75th percentile columns.
  • vcov() now returns the covariance matrix of the lavaan-side parameters and supports a type argument for choosing between sample and Laplace covariance.

Bug fixes

  • marginal_correction = "shortcut" no longer produces incorrect volume corrections.
  • qsnorm_fast() no longer incorrectly handles sign symmetries.

INLAvaan 0.2.3

CRAN release: 2026-01-28

  • Improved axis scanning, skewness correction, and VB mean correction routine.
  • Bug fixes for CRAN.
  • Updated README example.

INLAvaan 0.2.2

CRAN release: 2026-01-27

  • Under the hood, use lavaan’s MVN log-likelihood function to compute single- and multi-level log-likelihoods.
  • Added support for multi-level SEM models.
  • Added support for binary data using PML estimator from lavaan. NOTE: Ordinal is possible in theory, but the package still lacks proper prior support for the thresholds.
  • Added support for missing = "ML" to handle FIML for missing data.

INLAvaan 0.2.1

  • Support for lavaan 0.6-21.
  • Implemented variational Bayes mean correction for posterior marginals.
  • Defined parameters are now available, e.g. mediation analysis.
  • Prepare for CRAN release.

INLAvaan 0.2

  • INLAvaan has been rewritten from the ground up specifically for SEM models. The new version does not call R-INLA directly, but instead uses the core approximation ideas to fit SEM models more efficiently.
  • Features are restricted to normal likelihoods only and continuous observations for now.
  • Support for most models that lavaan/blavaan can fit, including CFA, SEM, and growth curve models.
  • Support for multigroup analysis.
  • Added PPP and DIC model fit indices.
  • Added prior specification for all model parameters.
  • Added support for fixed values and parameter constraints.
  • Initial CRAN submission.

INLAvaan 0.1

  • Used rgeneric functionality of R-INLA to implement a basic SEM framework.