INLAvaan now fits two-level models with random slopes, which lavaan specifies
with the rv() modifier. They work with compare(), loo(), waic(),
logLik(), predict(), simulate() and sampling(), and summary() marks
the rows that carry a random slope. fitted(), residuals() and the
standardised estimates use the implied moments averaged over the covariates,
and per_cluster = TRUE gives those of each cluster. The Bayesian fit indices
are scaled against an unrestricted random-coefficient model. The posterior
predictive p-value is not available and is dropped from test. A random
slope on a latent or split covariate uses lavaan's quadrature, which is
slower, and integration.ngh sets its number of nodes. Such fits have no
simulate(), casewise values, predict(type = "yhat"),
per_cluster = TRUE or Bayesian fit indices. Composites cannot be combined with random slopes yet. See the
multilevel article.
INLAvaan now fits composites, which lavaan specifies with the <~ operator,
with continuous data, in one or more groups or at either level of a two-level
model. They work
with the fit measures, loo(), compare(), predict() and sampling(),
and summary() gives posterior summaries for the variance and intercept of
each composite. Free weights have the new default prior
wmat = "normal(0,10)" in priors_for(). Unlike lavaan, INLAvaan estimates
the (co)variances of the indicators by default (composites.cov = "free");
composites.cov = "fixed" fixes them at their sample values. See the new
article on composites.
Composite models that INLAvaan cannot fit yet stop with an error: ordinal
data, and composites.cov = "fixed" in two-level models. So do composite
specifications that cannot be estimated as written, such as a free latent
mean that only composites measure (as in agrowth() on composites).
The posterior predictive p-value of two-level models was often close to 0
for models that fit. It now follows blavaan's two-level PPP: each posterior
draw generates replicate data, which are scored against the saturated model.
The new arguments ppp_method and ppp_nsamp set how the data are scored
(by default one Fisher-scoring step towards the saturated fit) and how many
draws are used (250). The two-level PPP is experimental.
simulate() on a two-level fit now keeps the clusters of the data, with
their labels and sizes, and no longer warns on every data set.
BCFI was -Inf or NaN for posterior draws whose independence model
fits within its degrees of freedom. Such draws now follow lavaan's CFI
convention.
standardisedsolution() ignored its nsamp argument and summarised as many
posterior draws as the fit itself had used.
Multigroup models with missing = "ml" gave wrong posteriors.
loo() and waic() gave wrong values for complete-data two-level models in
which the two levels list the variables in different orders, for example
when a regression appears at one level only.
compare() failed when a model was given as a long call.
sampling(prior = TRUE) with type = "observed" or type = "all" rejected
every draw in models with an observed outcome or covariate.
The marginal log-likelihood was too low by about half the number of free
parameters when vb_correction = TRUE (the default). As a result, Bayes
factors from compare() favoured models with fewer parameters.
A defined parameter (:=) can now use other defined parameters, in any
order, as in lavaan. Previously the fit failed with an "object not found"
error.
Defined parameters that are constant, or that cannot be computed for some
posterior draws (such as log(b) when b can be negative), no longer stop
the fit. Their summaries use the draws where they can be computed, and a
single warning gives the share left out, in place of R's repeated "NaNs
produced" warnings.
summary() showed the wrong SD, credible interval and prior for defined
parameters, and for some rows after them, when the model had equality
constraints (shared labels or group.equal).
Equality constraints written as a == b or a == <number> are now
honoured. Previously they were ignored, along with shared labels and
group.equal in the same model. Constraints INLAvaan cannot fit (such as
a == 2*b, a > 0, effect.coding, or a loading held equal to a variance)
now give an error.
Covariances held equal (by shared labels or group.equal) now fit
correctly. Previously their posterior was biased, or the fit failed.
sampling() and simulate() now work for models with equality
constraints, and simulate() for multigroup models.
Parameter names now match coef(). Previously plot(fit, params = ) could
not find multigroup := parameters, and labels such as lag1 were mangled.
A := parameter that reuses a parameter label, and multigroup two-level
models, now give an error.
Data with ordered-factor columns are now fitted as ordinal data, as with
ordered =. Previously the fit failed. ordered = naming variables outside
the model no longer widens the posterior, and a name that matches no model
variable gives an error.
predict(newdata = ) for ordinal models now codes the categories as in the
fit. Previously factor columns gave an error and 0/1 codes gave wrong
factor scores.
Chained modifiers in the model syntax are now all kept. Previously only one
survived, so prior("normal(0,1)")*a*x2 lost its prior, 0.5*a*x2 was
left free and NA*a*x1 stayed fixed.
A prior() on any of several parameters held equal is now used. Previously
only a prior on the first of them counted.
Models where only one variance enters a covariance (for example, with the other variance fixed) no longer fail.
predict() now works for models with observed covariates or observed
outcomes, including with conditional.x = TRUE. Factor scores and fitted
values for models with latent regressions now match lavaan.
Two-level models with named levels (level: within / level: between) now
fit, and their parameter names follow coef().
vcov() now matches coef() in length, order and names for models with
equality constraints, and lavInspect(fit, "vcov") works on such fits.
predict() no longer fails when a variance is zero, for example a residual
variance fixed to zero, or a latent variable fully determined by such an
indicator.
loo() and waic() no longer fail on two-level models fitted with
missing = "ML" that have between-level variables, such as cluster-level
covariates.
loo() and waic() now score two-level fixed.x = TRUE models correctly
when they mix covariates at both levels with within-only ones.
fit_skew_normal_samp(), and with it the summaries of covariances and
defined parameters (:=), missed the skew of draws with a small SD (below
about 0.2). Posterior modes are also more accurate for parameters on a small
scale.
loo() and waic() on FIML fits with fixed.x covariates now score the
outcomes given the covariates, as for complete data. Previously two-level
FIML scores included the covariates' density, so they could not be compared
with listwise fits or across covariate sets.
loo() and waic() no longer fail on FIML fits with singleton or two-row
clusters, or with clusters or rows that have no observed data. Units with
nothing to predict are left out, with a message. In two-level
type = "loso", rows with no within-level data no longer each count the
whole cluster.
predict(type = "ypred") for two-level models now adds residual noise to
observed outcomes, not only to factor indicators.
predict() for two-level models now draws latent variables and missing
values from their posterior given all of the cluster's data, as for
single-level models. Previously it returned factor scores without their
uncertainty, and imputed each row without the rest of its cluster.
With fixed.x = TRUE (the default), the marginal likelihood and DIC now
condition on the covariates. Previously a covariate with no effect changed
Bayes factors from compare(), by a lot in two-level models where it varies
at both levels.
fitMeasures() overstated the chi-square of models without a mean
structure, and with it BRMSEA and the other Bayesian fit indices.
The PPP no longer fails for conditional.x = TRUE fits.
The PPP no longer overstates the fit of models with fixed covariates
(fixed.x = TRUE, the default). Its replicates varied the covariates, which
the model does not predict.
simulate() and predict(type = "ymis") now work for conditional.x = TRUE
fits. Previously simulate() rejected every draw and imputation failed.
sampling() for conditional.x = TRUE fits now draws the covariates too,
as for other fits. Previously it left out their effects.
Models with a free covariance above 1 at its starting value, such as
covariances between covariates under fixed.x = FALSE, no longer fail.
Parameter types missing from dp take their default priors, and a dp
without names gives an error.
A Hessian that is not positive definite at the posterior mode gives a clear error.
plot() explains that fixed and derived parameters have no posterior
density.
compare() warns when fits differ in vb_correction.
predict(newdata = ) warns about ordinal values not seen when fitting.
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.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:
marginal_method = "marggaus", or with
marginal_correction = "hessian" or "none".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:
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:
BRMSEA was 0, BGammaHat was 1 and BTLI was more than 1.
Two-level fits with missing = "ml" gave NA.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:
baseline.model to supply a different baseline, or
baseline.model = FALSE to skip the incremental indices.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:
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.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 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:
vb_shift_max, the largest VB mean correction in posterior-SD
units, and scan_end_mass_max, the largest scan_end_mass.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.
NA unless marginal_method = "skewnorm".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:
"ppp", "dic", "loo" and "waic"."standard" (or "default") is c("ppp", "dic"). "full" is all four.
"none" is nothing.test = c("standard", "loo")."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:
nsamp argument is removed. If you supply it, you get a warning.second_order argument works as in loo(). At first order, the
WAIC and the LOO are identical.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.lpd term exists for all units
(k_min > -1). If not, waic() gives all estimates at first order, with
a warning.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.
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.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.
timing() function did not return the correct total time due to a
breaking name change in lavaan.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.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.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.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(...).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)inlavaan() fit calls.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.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.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.
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.meanstructure = FALSE for a two-level model now warns and
fits with meanstructure = TRUE (the mean structure is required there).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").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.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.dp argument of inlavaan() and friends is now documented in terms of
priors_for().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.solve() for
covariance and log-determinant calculations.samp_copula = TRUE), ensuring posterior samples have correct
skew-normal marginals and correct Pearson correlations.{qrng} for larger sequences. QMC sample size now scales with
model dimension.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.marginal_correction = "shortcut" no longer produces incorrect volume
corrections.qsnorm_fast() no longer incorrectly handles sign symmetries.missing = "ML" to handle FIML for missing data.rgeneric functionality of R-INLA to implement a basic SEM framework.