NEWS

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INLAvaan 0.2.4.9001

• Performance & Stability: Major optimizations to the gradient calculation for GCP models. The Jacobian computation now uses a sparse matrix approach, leading to significant speedups. The $\gamma_1$ correction calculation was corrected by reverting to the scalar path, ensuring numerical stability and accuracy.
• Internal: Added joint_lp_grad and joint_lp_grad_vectorised to the output list for external inspection.

INLAvaan 0.2.4 (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 (2026-01-28)

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

INLAvaan 0.2.2 (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.