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GMLTM_corr(): a variant of GMLTM()
that replaces the independent prior on theta (Sigma = sigma^2 * I) with
a multivariate Normal prior with a general correlation matrix Sigma
among cognitive components, estimated via a Cholesky factor with an LKJ
prior (argument lkj_eta). Implements innovation (iii) of
the GMLTM-D as formulated in Ramirez et al. (2024): admitting
correlations between components. Returns EAP$Sigma and
quantiles$Sigma in addition to the elements already
returned by GMLTM().extract_correlation(): extracts the posterior
correlation matrix Sigma from a GMLTM_corr fit, reports
pairwise credible intervals for each pair of components (flagging
whether the interval excludes 0), and draws a correlation heatmap
consistent with the style used in
generate_Q_with_interactions().compute_model_validation() now labels models by class
in $Summary and $Comparison
(e.g. "GMLTM-D (Sigma libre)" for GMLTM_corr
fits, "GMLTM-D (Sigma=I)" for GMLTM fits,
"MLTM-D", "LLTM"), so GMLTM,
GMLTM_corr, MLTM, and LLTM fits
can be freely mixed in the same comparison list.student_report(): builds a per-component and
per-rule mastery report for a single examinee (theta EAP and credible
interval, mastery cutlines gamma_m/tau_km,
decision-confidence indices, and rule/item difficulty), following the
mastery-diagnosis procedure for the MLTM-D in Embretson (2019). Includes
a scannable badge-style summary plot (report$plots$badges)
alongside the existing per-component continuum plots. This function was
previously named informe_estudiante(); that name is kept as
a deprecated alias and will be removed in a future release.student_report_batch(): pages
student_report() over many examinees. Always computes the
lightweight numeric summary (theta EAP, component/rule mastery and
confidence) for every requested student, but only builds the
ggplot2 plots for the students in the current
offset/limit page, so large batches stay cheap
to page through.priors$alpha in
GMLTM()/GMLTM_corr() now accepts a
family element, either "normal" (default;
half-Normal, unchanged) or "lognormal" (Log-Normal),
letting users match the alpha prior family actually used when fitting.
New prior_predictive_check() simulates data directly from a
declared priors specification (including
alpha$family) under the GMLTM-D’s generative structure,
before any data is fitted, following the prior predictive checking
workflow of Gelman et al. (2020, Sect. 2.4);
plot_prior_predictive_check() visualizes the resulting
proportion-correct distribution against a configurable substantively
plausible range.GMLTM(), GMLTM_corr(),
MLTM(), and LLTM(): when data was
a plain matrix (as opposed to a data.frame),
the response vector was built via unlist(data), which does
not flatten a matrix (its dim attribute is left untouched)
and made model fitting fail with a dimension-mismatch error from Stan.
Fixed by flattening with as.vector(as.matrix(data)), which
produces the same column-major order for both data.frame
and matrix inputs.reliability.R and
conditional_reliability.R were split into smaller,
topic-focused files (reliability-enhanced.R,
reliability-diagnostics.R,
conditional-reliability-compare.R); related exported
functions are now grouped via @family tags in their
documentation; and a package-level overview page (?GMLTM)
was added.inst/.GMLTM1() and GMLTM2() have been removed.
Their functionality is fully covered by the priors argument
of GMLTM(). See the vignette for equivalent prior
specifications.priors argument.rstan for full CRAN
compatibility.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.
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