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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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.