NEWS.md

In INLAvaan: Approximate Bayesian Latent Variable Analysis

INLAvaan 0.3.2

Deprecations

• The Sigma argument of loo() is now Omega. The new name agrees with the notation for the posterior covariance in the documentation. You can still use Sigma, but you get a deprecation warning. If you supply the two names, you get an error.

Bug fixes

• Single-level fits with missing = "ml" gave incorrect posterior summaries with the default skew-normal marginals. The loadings and variances were far from the FIML estimates, and the scan-endpoint check flagged almost all parameters. The cause was a shortcut that treats the free intercepts as separate from the covariance parameters. This is correct for complete data, but not under FIML. INLAvaan no longer uses the shortcut under FIML. These results were already correct:
• The posterior mode and the Laplace covariance.
• Fits with marginal_method = "marggaus", or with marginal_correction = "hessian" or "none".

• Two-level FIML fits.

• The posterior predictive p-value (PPP) was incorrect for data with missing values. For example, a correct model with 20% missing cells got a PPP of 0.000. The cause was that the PPP used a sample covariance that is not correct when values are missing. The PPP now uses the saturated (h1) covariance from lavaan:

• For single-level FIML fits, this is the EM covariance.
• For two-level fits, these are the within-level and between-level h1 covariances, with or without missing values.

Single-level fits with complete data do not change. Two-level fits with complete data get slightly different PPP values.

• The Bayesian fit indices had two errors:
• For two-level models, the saturated log-likelihood was much too low. The deviance chi-square became negative, and INLAvaan set it to zero. As a result, BRMSEA was 0, BGammaHat was 1 and BTLI was more than 1. Two-level fits with missing = "ml" gave NA.
• The count of sample moments included the moments of fixed exogenous covariates, but lavaan does not count these. As a result, BRMSEA and adjBGammaHat used slightly incorrect degrees of freedom for models with an observed predictor and fixed.x = TRUE.

INLAvaan now gets the two values from lavaan. Single-level fits without covariates do not change.

• The incremental fit indices BCFI, BTLI and BNFI used an incorrect baseline. compare() used its first model as the baseline, without a warning. Thus the first model got a score against itself, which is always zero, and the other models got a score against the first model, not against a null model. fitMeasures(), bfit_indices() and compare() now fit the independence model automatically, as lavaan does. In this model, each observed variable has its variance and intercept, and no variables correlate. The fit uses the same data and options as the model, and takes less than one second, also for 64 items. compare() fits it one time for all models. Other changes:
• Use baseline.model to supply a different baseline, or baseline.model = FALSE to skip the incremental indices.
• If baseline.model has the same free parameters as the model, you get a warning.
• BTLI is now NA, not -Inf, when the ratio of the baseline is 1.

The absolute indices (BRMSEA, BGammaHat, adjBGammaHat, BMc) do not change.

• For two-level models, sampling() gave only the within-level part. All three types now draw from the full two-level model:
• type = "latent" now includes the between-level latent variables.
• type = "observed" now includes the between-level part and the between-only variables.

• type = "implied" now gives the within-level and between-level covariances, not one covariance.

• With marginal_method = "marggaus", the Mean and SD were incorrect for parameters on a transformed scale, such as variances and correlations. The Mean was the posterior median, and the SD came from the delta method. INLAvaan now calculates the two values as the moments of the transformed Gaussian marginal, with Gauss-Hermite quadrature. The quantiles, modes and densities do not change.

• predict() did not draw its parameter sample in the same way as the rest of the package. It did not use the NORTA correlation adjustment, and it ignored the samp_copula setting of the fit. Thus the factor scores and predicted values had a different dependence structure from the posterior draws of the fit. predict() now uses the stored correlation matrix and the samp_copula setting of the fit.

• timing() gave a total that was too large. It added the lavaan setup time two times, because this time is already part of init. The segments now do not overlap, and their sum agrees with system.time(). timing() no longer shows the start_time stamp as a duration. The documentation now includes the loo and waic segments.

• loo() removed the units without a second-order term from elpd_loo, p_loo and their standard errors. Thus the sum had fewer units, and the model looked better than it is. loo() now keeps all the units:

• If the log CPO term of a unit does not exist at second order (k_max >= 1), loo() gives all estimates at first order, for all units. Thus each estimate uses one order only. loo() and fitmeasures() give a warning that names these units.
• If the lpd term of a unit does not exist at second order, the unit adds its first-order difference to p_loo. elpd_loo and looic do not change. This case is usual in SEM fits and the error is small, thus there is no warning. The printed result shows a note, and n_lpd_ok gives the count.

This changes elpd_loo and looic for fits with a unit at k_max >= 1. Without such a unit, the other data and the prior cannot identify some combination of the parameters. Thus, examine such units.

• compare() calculated se_diff from per-unit values that did not agree with elpd_diff. The paired variance did not include the units without a second-order term, but the ELPD totals included them. The two values now use the same per-unit values.

• compare(loo = TRUE) now uses the same Taylor order for all models. This is the highest order that all the models can supply. Before, it could compare a model at second order with a model at first order. Thus part of elpd_diff came from the change of order, not from the models. The table shows the order.

• test = "loo" stored the LOO but not the WAIC, but the documentation said that it stores the two. A request for the LOO or the WAIC now stores the two (see the test entry under New features).

New features

• New vb_method argument to inlavaan(), acfa(), asem() and agrowth() sets the integration rule for the VB mean correction:
• "sobol" (the default) uses the scrambled Sobol rule with n_qmc nodes, as before.
• "gauss_hermite" uses a deterministic three-point Gauss-Hermite rule along each principal axis of the Laplace covariance. This is 2m + 1 nodes for m free parameters.

The Gauss-Hermite rule gives the same shift on each run. On the benchmark models, it was more accurate than the default 64-node rule. It is faster than the default for fewer than approximately 30 free parameters, and slower for more. It gives no quadrature error, thus vb_mcse_sigma is NA. This feature is experimental.

• diagnostics() has new values:
• Global: vb_shift_max, the largest VB mean correction in posterior-SD units, and scan_end_mass_max, the largest scan_end_mass.

• For each parameter: scan_end_mass (see the next entry), and alpha, the shape of the fitted skew-normal.

• New diagnostic scan_end_mass. It is the probability that the fitted skew-normal marginal puts outside the scan window, which is four posterior SDs on each side of the mode. INLAvaan fits the marginal only inside this window, thus the mass outside it is an extrapolation. A large value shows that the credible limits of the parameter are not reliable.

• A Gaussian marginal gives 6.3e-05. Good fits give values from 1e-03 to 1e-02.
• The fit gives a warning if a parameter has a value more than 0.05. The warning names a maximum of three parameters.
• The value is NA unless marginal_method = "skewnorm".

• The calculation is closed-form, thus it adds no cost.

• New samp_norta argument to inlavaan(), acfa(), asem() and agrowth() enables or disables the NORTA correlation adjustment of the skew-normal copula. It is independent of samp_copula. The default is FALSE, because the adjustment does not change the marginals and has a very small effect: on the benchmark models, it changed no correlation by more than 0.01. predict() uses the same setting as the fit.

• The test argument of inlavaan(), acfa(), asem() and agrowth() now gives the set of post-estimation values to calculate:

• The basic values are "ppp", "dic", "loo" and "waic".
• "standard" (or "default") is c("ppp", "dic"). "full" is all four. "none" is nothing.
• You can combine values, for example test = c("standard", "loo").
• An unknown value gives an error. This includes the test names of lavaan, for example "satorra.bentler". Before, lavaan ignored these values without a warning.

You can now request the PPP and the DIC separately. For example, test = "dic" gives the DIC without the PPP.

The default no longer calculates the LOO and the WAIC. Before, the default calculated the LOO if its predicted time was less than 10 seconds. Thus the fit time was difficult to predict. INLAvaan now calculates the LOO and the WAIC only on request ("loo", "waic" or "full"). If the model does not support them (PML or ordinal data, conditional.x = TRUE, multigroup two-level models), you get a warning and the fit continues without them.

To get elpd_loo in fitmeasures(fit) as before, use test = "full" or add_loo(fit). add_loo() now stores the LOO and the WAIC (before, it stored only the LOO). loo(fit) and waic(fit) still calculate on request.

• cores > 1 now works in all front ends. Before, the parallel stages of inlavaan() and loo() used mclapply() to fork worker processes. Forks are not available on Windows. They are also not safe in threaded IDE sessions, such as RStudio and Positron, where the child processes can stop without a message. INLAvaan now uses a PSOCK cluster (separate R processes) when forks are not safe.

• New cov_as_cor argument to inlavaan(). INLAvaan always estimates the residual and latent covariances (theta_cov, psi_cov) on the correlation scale. By default, it then reports them on the covariance scale from a posterior sample, as lavaan and blavaan do. With cov_as_cor = TRUE, INLAvaan reports the correlation-scale marginals directly, as theta_cor and psi_cor. Use this option to compare the marginals with a reference on the correlation scale. The estimation does not change.

• waic() is now deterministic. It calculates the two WAIC terms in closed form from the Laplace summary, with the same per-unit Taylor values as loo(). It no longer uses posterior draws. This changes the estimand, thus p_waic and waic change for all existing fits. The change is largest at small N. Other changes:

• The results are exactly reproducible, and do not depend on a seed.
• The nsamp argument is removed. If you supply it, you get a warning.
• The new second_order argument works as in loo(). At first order, the WAIC and the LOO are identical.
• The p_waic > 0.4 rule is removed. It was an empirical threshold for the variation of the old estimator, with no theory to support it.
• The second-order WAIC exists only if the lpd term exists for all units (k_min > -1). If not, waic() gives all estimates at first order, with a warning.
• At fit time, the WAIC comes from the same calculation as the LOO, at no extra cost.

• The per_unit table of loo() has two new columns: k_ssq, which is part of the WAIC penalty, and k_min, the existence check for the lpd term.

• loo() now gives two curvature diagnostics for each unit, k_max and k_sum. It calculates them in closed form from the Laplace summary, not from posterior draws. k_max is the fraction of the posterior precision that the unit has along its worst direction. The second-order term of the unit exists only if k_max < 1. k_sum is the total leverage of the unit. loo() applies no threshold to these values.

Minor improvements and fixes

• loo() no longer uses the name "effective number of parameters" for two different values:
• p_loo has the same definition as in the loo package. It stays in the estimates table.
• The sum of the per-unit k_sum is now pd_trace, the trace form of the DIC pD.

The printed result also shows a new curvature check. It compares the total gap between the first-order and second-order estimates (elpd_gap) with half of pD. A large excess shows that the second-order expansion is not stable. The documentation now also tells you to use second_order = FALSE only for diagnostics or to decrease cost. A first-order score is too high by approximately half of pD, thus it cannot compare models of different sizes.

• loo() and waic() results now print under a cli rule that shows the number of units, the number of groups and the Taylor order. This rule replaces the "Computed from ..." line. The notes now fit the width of the console. summary() on these results is the same as print().

• INLAvaan now requires lavaan >= 0.7-2. Thus the compatibility layer for older lavaan versions is removed. The warning about the two-level FIML gradient for cases with all within-level values missing is also removed, because lavaan >= 0.7-1.2707 corrects this problem.

• The default "nlminb" optimiser now uses iter.max = 1000 and eval.max = 2000. The nlminb() defaults (150 and 200) were too small for some complex models. When the optimiser reached these limits, only diagnostics() or a fit-time warning showed it. Values that you supply in control still have priority.

INLAvaan 0.3.1

Bug fixes

• The timing() function did not return the correct total time due to a breaking name change in lavaan.
• Fixed CRAN errors and notes on certain linux builds relating to .Rd usage and convergence checks.

INLAvaan 0.3.0

New features

• loo() computes leave-one-out cross-validation from a single fit without refitting nor sampling, via a Taylor approximation of the case-deletion posterior: per-subject (LOSO) for single-level models, per-cluster (LOCO) for two-level models. Reports first- and second-order estimates and pointwise contributions, with opt-in parallelism (cores) and theta/Sigma overrides for scoring conditioned posterior summaries in user-built model-search workflows.
• waic() computes the widely applicable information criterion from posterior draws, with pointwise contributions and reliability warnings.
• Both criteria score fits with exogenous covariates on the likelihood they were fitted with: jointly with the covariates (fixed.x = FALSE) or conditionally on them (fixed.x = TRUE, the lavaan default; exact, no additional approximation), for any covariate placement, including cluster-level and within-level covariates in two-level models. The two flavours are never comparable, as conditional comparisons may differ in their covariate sets, which enables covariate selection.
• loo() and waic() support multigroup models. Groups are independent, so each unit is scored against its own group's implied moments, under either mean treatment and either covariate flavour, with cross-group equality constraints (group.equal) flowing through automatically. Units are identified by case number and carry a group column, so results keep their identity across fits that stack groups differently. Multigroup two-level models are not supported yet.
• loo() and waic() support fits estimated by full-information maximum likelihood (missing = "ml"). Single-level units are scored on the entries they actually have -- the observed-data predictive log p(y_i,obs | D_-i) -- with casewise kernels evaluated per missing pattern, so a unit with fewer observed entries self-weights in the elpd. Two-level fits are scored per cluster (LOCO), each cluster on its observed-data marginal likelihood via lavaan's raw-data cluster kernels (no per-cluster sufficient statistics, since LOCO deletes whole clusters). This shares the missing-at-random assumption of the FIML fit itself. Multigroup two-level models remain unsupported under missingness.
• On two-level models loo() and waic() gain type = "loso", scoring the conditional predictive (leave-one-unit-out: a new observation within an observed cluster) instead of the default marginal predictive (type = "loco", leave-one-cluster-out: a new cluster). These are the two estimands of Merkle, Furr & Rabe-Hesketh (2019); they answer different questions and are easily conflated, so the marginal is the default and the conditional warns. loo() uses the Taylor expansion and waic() the posterior draws, computing the same estimand two ways; both work with and without missing data. (waic() previously had no type.)
• fitmeasures() gains elpd_loo, se_loo, p_loo, looic and elpd_waic, se_waic, p_waic, waic: included in "all" when stored with the fit, computed on demand when requested by name.
• compare() gains loo = TRUE. Models sorted by descending ELPD, with p_loo and ELPD differences with paired standard errors (mixed-flavour comparisons are refused). Pairing matches units by id rather than row order, so a pooled fit can be compared against a multigroup fit of the same data, and the measurement-invariance ladder (configural, metric, scalar) is compared on a proper predictive scale.
• Both criteria can be computed at fit time and stored with the fit. The default test = "standard" does so automatically for supported models with a mean structure. The WAIC reuses the fit's own posterior draws (when nsamp >= 100), and the LOO runs when its predicted serial cost is within a 10-second budget. test = "loo" forces the LOO regardless of the budget, test = "none" skips everything, and fit <- add_loo(fit) stores it post hoc. Stored results are reused by loo(), waic(), fitmeasures(), and compare().
• fitted() (and fitted.values()) return the model-implied moments of an INLAvaan fit, evaluated at the posterior means, matching the lavaan and blavaan output structure. type = "ov" gives casewise predicted values.
• predict() gains a summary argument; summary = TRUE collapses the posterior draws and returns point estimates directly, equivalent to summary(predict(...)) in one call. Default FALSE, so existing code is unaffected.
• residuals() (and resid()) return the observed-minus-fitted moments of an INLAvaan fit, matching the lavaan and blavaan output structure and supporting all lavaan residual types (raw, cor, cor.bentler, normalized, standardized) plus type = "casewise".
• anova() on an INLAvaan fit now errors, pointing to compare(). Unlike fitted()/residuals()/predict(), this is a deliberate departure from blavaan (which silently inherits lavaan's frequentist likelihood-ratio test): there is no direct Bayesian analogue of that test, and compare() already provides the appropriate tools (Bayes factors, DIC/pD, LOO/WAIC).
• logLik() returns the Laplace-approximated marginal log-likelihood (log evidence) by default, printed with a note that it is not comparable to a classical log-likelihood; type = "plugin" instead returns the classical log-likelihood at the posterior mean, with df/nobs attributes so it supports AIC()/BIC() at the point estimate.
• deviance() is new for INLAvaan fits (lavaan has no deviance() at all). Follows the BUGS/JAGS/Stan convention: type = "mean" (default) returns the posterior mean deviance with pD/DIC attached as attributes; type = "plugin" returns the deviance at the posterior mean (matching -2 * logLik(type = "plugin")). Both require test != "none".
• AIC()/BIC() on an INLAvaan fit now error, documented alongside logLik(). Both are large-sample asymptotic approximations to quantities INLAvaan already computes directly -- AIC approximates predictive accuracy (loo()/waic()), BIC approximates -2 * log(marginal likelihood) (logLik()) -- so reporting them at the posterior mean would be a cruder proxy for numbers already available. The point estimate remains available for reporting-convention purposes via AIC(logLik(object, type = "plugin")) / BIC(...).
• Fits now self-check their diagnostics: inlavaan() warns once, at the end of the fit, if the optimiser did not converge, the gradient at the reported mode is materially non-zero (Newton step > 0.1 posterior SD), a skew-normal marginal fits poorly (NMAD > 0.1), the VB correction shifted a posterior mean by more than 1 posterior SD, or the Hessian is near-singular -- naming the offending parameters. A healthy fit stays silent; suppress via the inlavaan_diagnostics_warning condition class. diagnostics() gains the scale-free mode_shift_max (global) and mode_shift_sigma (per-parameter) measures backing the gradient check. (#18)

Minor improvements and fixes

• Saturated-means fast path: when the mean structure is saturated (all intercepts free and unconstrained with normal priors, no nonzero latent means), the posterior is exactly block-diagonal between the intercepts and the covariance parameters at the mode. The Hessian intercept block is now computed analytically with an exact zero cross block (finite differences run over the covariance columns only), and the skew-normal marginal scans skip the intercept axes, emitting their exact Gaussian marginals directly. About 25% faster on typical CFA/SEM fits, with results identical to within finite-difference noise.
• Improved messaging for inlavaan() fit calls.

Bug fixes

• standardisedsolution() and summary(standardized = TRUE) no longer silently drop their arguments under lavaan >= 0.7-1, which renamed several exported arguments (e.g. cov.std to cov_std, GLIST to glist). INLAvaan now resolves the spelling the installed lavaan expects once per session at load, working across lavaan versions.
• Two-level FIML loo()/waic() scores are now correct for clusters containing a case fully missing on the within-level variables. lavaan retains such cases but its analytic gradient kernel mishandles the zero-observed pattern; INLAvaan drops these rows before the cluster kernels (exact for the marginal likelihood). Two-level FIML fitting also inherits the upstream gradient issue (fixed in lavaan PR #581), so inlavaan() warns when such cases are present on lavaan versions before the fix.
• Models fitted with meanstructure = FALSE now use a proper Bayesian likelihood. See "Mean structures" vignette for details, including when model comparisons across the two mean treatments are meaningful.
• The saturated means are given flat priors and marginalised analytically (closed form), replacing lavaan's profiled likelihood, which is not a valid Bayesian object.
• Posterior modes recalibrate by the factor n/(n-1) on the covariance side.
• loo() and waic() score such fits on the exact exchangeable case-deletion conditionals. The previous zero-mean fallback and its warning are gone, and absolute ELPD values are meaningful and comparable with meanstructure = TRUE fits.
• Posterior predictive draws include the saturated means and their mean-uncertainty.
• Requesting meanstructure = FALSE for a two-level model now warns and fits with meanstructure = TRUE (the mean structure is required there).
• The conditional (fixed.x = TRUE) flavour — the default for SEM with exogenous covariates — is fully supported: the mean marginalisation factorises blockwise, so each unit is scored by the difference of two exchangeable conditionals, with the frozen-covariate term entering as an exact constant.
• predict() now centres the conditioning data on the model-implied means (or the saturated sample means when the model has no mean structure) when drawing factor scores and predicted observed variables. Previously the kernels conditioned on raw data, offsetting every factor score by a constant that grows with the variable means.
• sampling() and simulate() draws of observed variables from models without a mean structure now include the saturated (sample) means, so posterior predictive replicates live on the data scale instead of being centred at zero.
• sampling() and simulate() no longer error for models with a single latent variable, and their saturated-mean recovery is now robust to missing data (replicate columns were previously NA under missing = "pairwise").
• The PPP's observed discrepancy now uses the unbiased (divisor n-1) sample covariance, matching the scale of the Wishart-replicated covariances it is compared against; previously the divisor-n form made the PPP very slightly optimistic (an O(1/n) effect, all models).
• coef() (and the merged parameter table, fitted values, and implied moments) now reports covariance parameters on the covariance scale. Previously these slots carried the posterior-mean correlation, while summary() showed the correct sample-based covariance; the discrepancy is visible whenever the relevant standard deviations are far from 1.

INLAvaan 0.2.5

Minor improvements and fixes

• INLAvaan now works with both the current lavaan 0.6 series and the upcoming lavaan 0.7, which renames many of its internal functions. The lavaan internals INLAvaan relies on are now resolved when the package loads, under whichever naming scheme is available. lavaan (>= 0.6-19) is now declared explicitly, and the package is checked against the oldest supported, current CRAN, and development versions of lavaan on CI.
• Fixed the trapezoid rule used by compare_mcmc() for density normalisation, overlap, and KL divergence computations.
• compare_mcmc() and diagnostics() are now robust to NA values in density and diagnostic computations.
• The dp argument of inlavaan() and friends is now documented in terms of priors_for().

INLAvaan 0.2.4

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

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

INLAvaan 0.2.2

• 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.

INLAvaan documentation built on Oct. 2, 2026, 1:07 a.m.