New function alphaN_power() computes the power of
the calibrated test against a standardized effect of size
d: the design-time companion of alphaN()
(noncentral t for single coefficients, noncentral F for joint tests, at
the residual degrees of freedom). For model-specific effect
parameterizations (odds ratios, rate ratios), the calibrated alpha plugs
directly into the calculators of the pwrss package via their
alpha argument; see ?alphaN_power.
New function alphaN_power_plot() draws power against
sample size, one panel per effect size, with every calibration method
evaluated at its own alpha(n) and a fixed reference level as a dashed
curve.
alphaN_report() gains a power_at
argument: the settings report can now document the power of the
calibrated test against the effect sizes the researcher cares about,
alongside the alpha itself.
alphaN_report() now also states the scope of the
"ES" and "moment" calibrations: exact for the
normal linear model when p is supplied (evaluated at the
effective sample size n - p), the conservative large-sample
form otherwise, and asymptotic for other generalized linear
models.
New function alphaN_report() writes a
preregistration-ready Markdown settings report: every input behind a
calibrated alpha (sample size, evidence target, method, prior settings),
the resulting alpha, the decision rule, and the references to cite. It
mirrors the downloadable report of the companion Shiny application, can
write straight to a file, and hard-wraps its lines (default 72
characters, see width) so the report reads well in
consoles, files, and rendered documents.
JAB() called without a covariate now
returns a named vector with the Bayes factor of every coefficient except
the intercept (which remains available on explicit request).
alphaN_plot() and JAB_plot() have been
restyled: colorblind-safe Okabe-Ito palette, light grid lines, open
axes, and horizontal axis labels. alphaN_plot()
additionally gains a log argument (“x”, “y”, “xy”) which
helps when the fast-falling “moment” curve is drawn next to the others,
and its tick labels always use plain notation (“0.0001” and “10,000”,
never “1e-04”).
New function klauerBF() exports the effect-size and
moment Bayes factors of Klauer, Meyer-Grant & Kellen (2025) that
alphaN() inverts: from a t statistic (one-sample test or
single regression coefficient) or from an F statistic for a joint test
of q coefficients.
alphaN() (methods "ES" and
"moment") gains arguments q and
p. With q > 1 the alpha level is calibrated
for the joint F test of q coefficients through the exact regression-case
Bayes factors of Klauer et al. (2025, Table 4), implemented natively
including their Gaussian hypergeometric term; p sets the
number of retained model parameters so that small-sample calibrations
can use the effective sample size n - p (residual degrees
of freedom). The defaults (q = 1, p = 0)
reproduce the previous behavior exactly. The moment-prior default
nu is now 5 + (q - 1) and the ES-prior scale
recommendation generalizes to
r = sqrt((nu - 2)/(nu * q)) * de, both following the paper
(unchanged at q = 1).
New function n_effective() computes the effective
sample size n * (se/se_robust)^2 that Wulff & Taylor
(2024) recommend as a sensitivity check when calibrating alpha with
clustered (panel) data.
alphaN_plot() gains a methods argument
and can now draw the "ES" and "moment" curves
alongside the prior-fraction methods.
The regression-case implementation is validated against all printed Bayes factors in Table 8 of Klauer et al. (2025), in addition to the existing Table 7 anchors, and the q = 1 F form agrees with the validated t form to near machine precision.
The quadrature and inversion machinery is additionally
stress-tested against an independent oracle that integrates on the
original effect scale over an infinite range with a plain density ratio
(none of the package’s substitution, windowing, or log-clamping
choices); the normal-limit switch at n - p = 50,000 is
measured directly by running both branches at the same effective sample
size, and monotonicity of alpha in n and BF,
and of the Bayes factors in the test statistic, is checked over grids
that cross the switch, including joint tests and small residual degrees
of freedom.
?alphaN states the model scope of the "ES"
and "moment" methods: exact under the normal linear model,
asymptotic (like the prior-fraction methods) for other generalized
linear models.citation("alphaN") now also lists Klauer et al. (2025)
for users of the "ES" and "moment"
methods.alphaN() gains two methods based on Klauer, Meyer-Grant
& Kellen (2024, Psychonomic Bulletin & Review,
doi:10.3758/s13423-024-02612-2): method = "ES" calibrates
alpha to their effect-size Bayes factor, whose prior centers the
alternative hypothesis on a prespecified effect size, and
method = "moment" calibrates alpha to their moment Bayes
factor, under which effects near zero are a priori implausible. New
arguments de (targeted effect size, default 0.5),
nu, and r control the priors, with defaults
following the paper’s recommendations. Because the moment prior rules
out near-zero effects, the alpha level it implies falls much faster with
n than under JAB.method = "ES", nu = 1, de = 0 with
an explicit r calibrates alpha to the default
(Jeffreys-Zellner-Siow type) Bayes factor of Rouder et al. (2009).alphaN() and JABt() now return correct
results when n is a vector and
method = "robust" or method = "balanced".
Previously, "robust" silently applied the smallest sample
size to every element and "balanced" failed with an
unrelated error.method, a missing
df in JABp(..., z = FALSE), a p
outside (0, 1], a non-positive n or BF, and an
unknown covariate in JAB() (which now lists
the coefficients available in the model).JAB_plot() gained an upper argument,
passed on to the underlying computations for
method = "balanced".alphaN_plot() gained a ylim argument. The
default now covers all four curves; previously the y-axis was fixed to
(0, 0.05), which silently clipped the "balanced" curve for
small Bayes factors.JAB() now determines the sample size via
nobs().?JABp no longer has a placeholder title.JABp()
expects a two-sided p-value.NEWS.md file to track changes to the
package.