The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
A major release on two fronts: semTests now has a self-contained
numerical core with a minimal dependency footprint (Imports
is just lavaan and the base methods package),
and the eigenvalue-based p-values have been broadened well beyond
normal-theory ML (see ?semTests-support).
lavaan::lavTest() returns a named list of test results
rather than a single flat object (the reweighted least squares
chi-square statistic was previously read as NULL). The
statistic is now extracted in a way that works with both old and new
lavaan return structures.sum lambda_j chi^2_1 is
computed internally by imhof_pvalue(), which is more
accurate in the tail, where CompQuadForm degrades (its
imhof returns quadrature noise, including negative
probabilities, once the p-value drops below roughly 1e-6,
and davies underflows to zero). Positive spectra use
Ruben’s series (exact to machine precision, including the tail);
mixed-sign spectra use the Imhof integral in the body with a
Lugannani-Rice saddlepoint fallback in the tail.m x m restriction-space matrix instead of the full
q x q UGamma, where m is the number of
restrictions. The result is identical to machine precision but faster on
large models, and it removes the need for RSpectra.MASS::ginv with a
self-contained generalized_inverse().CompQuadForm, Matrix,
MASS, RSpectra, progressr, and
future.apply dependencies, plus the psych
suggestion (examples now use the built-in
HolzingerSwineford1939 data). Imports is now
lavaan and methods.pvalues() now supports GLS, ULS, and categorical WLSMV/DWLS
in addition to ML/MLM/MLR, plus FIML missing-data fits (single-group,
continuous); pvalues_nested() supports the continuous
estimators and nested FIML comparison (method = "2000").
See ?semTests-support. This broadened support is
experimental; the classical normal-theory ML path remains
stable.?semTests-support: non-lavaan objects,
unsupported estimators, unsupported missing-data modes, multi-group /
fixed-exogenous FIML, the normal-theory-only RLS statistic and the
Du-Bentler UG gamma off the classical case, and categorical
nested tests are now refused up front.semTests_pvalues object
that records the options actually used (estimator, statistic,
information type, gamma type, data type, degrees of freedom) and prints
a one-line provenance footer.semTests vignette and an
inst/CITATION.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.
Health stats visible at Monitor.