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drmeta_bootstrap_gamma() now short-circuits when the
constrained estimate already lies at gamma = 0. In that
situation the null and alternative fits coincide, so the
likelihood-ratio statistic is zero by construction and the simulated
null distribution has a large point mass at zero. Bootstrapping ties at
zero returns a value that reads like a p-value but only records the
proportion of replicates that also reached the boundary. The function
now returns statistic = 0, p.value = 1, and
B_used = 0 without running any replicates.
The returned object gains a logical boundary
component, TRUE when the short-circuit applied and
FALSE otherwise. print() explains in the
boundary case why no bootstrap was run and points to
constrained = FALSE as the appropriate directional
follow-up.
drmeta_bootstrap_gamma() gains a tol
argument controlling the threshold below which the observed
likelihood-ratio statistic is treated as exactly zero. Default
1e-8.Description field cites Self and Liang (1987) for
the boundary problem and Viechtbauer and Lopez-Lopez (2022) for the
general location-scale parent model, and states that a scale model
reweights studies rather than adjusting the mean for design-linked
bias.The package is repositioned as a constrained specialization of the meta-analytic location-scale model rather than a standalone “variance-function framework for causal credibility”. Documentation no longer describes the design index as a measure of causal credibility.
dr_heterogeneity() no longer returns the
design-residual variance decomposition or R2_DR. That
decomposition was not identified by the model and is superseded by
dr_scale_attenuation(), which is explicitly a description
of the fitted scale function and not a proportion of heterogeneity
explained.
The following functions from 0.1.0 have been removed:
dr_variance(), dr_weights(),
dr_forest(), dr_funnel(),
dr_plot(), and dr_pub_bias().
dr_variance() is superseded by
dr_scale_predict(); study weights are available as the
weights component of a fit. The publication-bias and
plotting functions were removed pending revision against the corrected
specification.
drmeta() gains mods,
constrained, gamma_max, and
gamma_fixed arguments. The pooled effect is now returned as
beta, a named vector of location coefficients, rather than
a scalar mu.
Location moderators and the joint design-indexed location-scale model.
Exact estimation at the boundary gamma = 0, via
direct optimization over a finite interval plus an explicit boundary
comparison.
Unrestricted scale fits (constrained = FALSE) as a
directional diagnostic; a negative estimated gradient contradicts the
substantive constraint rather than merely failing to support
it.
drmeta_bootstrap_gamma(), a parametric-bootstrap
test of the boundary null.
dr_scale_predict() and
dr_scale_attenuation() for observed-support summaries of
the fitted scale function.
dr_loo() for leave-one-out influence on both the
location and scale components.
dr_plot_vfun() for the fitted variance function,
drawn over observed support by default.
confint() and logLik() methods.
logLik() reports the criterion actually used and refuses to
relabel a REML fit as ML.
Warnings when the design index has fewer than three distinct values, when its observed range is narrow, and when the estimated gradient sits at the optimization bound.
The bundled BCG example data used a DR column
alongside dr_weight, so the documented bcg$dr
silently partial-matched onto dr_weight and fed fitted
weights into the model as the design index. The column is now named
dr, the stale weight columns are removed, and examples
index with [[.
Roxygen blocks in the diagnostics file were separated from their
functions by a blank line, so regenerating the documentation would have
dropped dr_scale_predict(),
dr_scale_attenuation(), and dr_heterogeneity()
from the namespace.
All exported functions now document a return value.
The default gamma_max is 8, matching the
optimization range reported in the accompanying manuscript. It was
20.
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.