prior_conflict() now computes
box_pvalue (and the s_value derived from it)
from the exact prior predictive distribution by default
wherever a closed form exists: Beta-Binomial (Beta prior,
type = "binary") and Gamma-Poisson / Negative Binomial
(Gamma prior, type = "poisson" or "survival").
The exact argument now defaults to NULL (exact
where available, silent fallback to the Normal approximation otherwise);
exact = TRUE additionally messages on fallback and
exact = FALSE reproduces the Normal-approximation results
of 0.4.0 and earlier. The exact check requires whole-number counts (and,
for binary data, a whole-number sample size); otherwise the Normal
approximation is used. $box_pvalue_method reports which was
used.
Reason: a simulation study (see validation/) found that
the Normal approximation, whose standard error is estimated from the
observed proportion or count, can reject far more often than its nominal
level when there is no prior-data conflict, for informative priors and
small samples. For example, for a Beta(5, 45) prior with n = 10 and no
conflict, the approximation flagged conflict in about 39% of simulated
datasets at the 5% level, because zero observed events (probable under
the prior) yield a zero standard error and a small approximate p-value.
The exact version’s rejection rate stayed at or below the nominal level
in all simulated settings. Results for moderate samples and weakly
informative priors are essentially unchanged.
The prior predictive p-value now also covers mixture
priors (e.g. robust_prior(), and linear pooling
from aggregate_experts()): the predictive is the weighted
mixture of the component predictives, exact for Beta (binary) or Gamma
(Poisson/survival) components. Other proper priors (Normal, lognormal,
Weibull, exponential, or a Normal vague component) are handled by
equal-probability quadrature over the prior, truncated to the parameter
space; $box_pvalue_method is then "numerical".
The definition of the p-value is the same throughout (total predictive
probability of outcomes no more probable than the observed one). In the
simulation (validation/sim_mixture.R), pooled-consensus
mixtures with the Normal approximation rejected up to 43% of the time at
the 5% level under no conflict; the prior predictive version never
exceeded 5%. For lognormal, Weibull and exponential priors on a rate
(validation/ sim_numerical.R, 84 cells) the numerical
p-value never exceeded the nominal level either (the approximation
reached 63%). At equal size, the prior predictive p-value has the same
average power as the old approximation, but less power against downward
shifts for skewed priors (validation/sim_power.R; see
REPORT_boxp.md for the full comparison).
New field $box_pvalue_caution on
prior_conflict() results: set when the Normal approximation
produced the p-value for discrete data with zero or all events, or fewer
than 10 events or non-events. Shown in print() and in the
app.
conflict_severity is now classified on the reported
box_pvalue: "severe" below 2 * pnorm(-3)
(about 0.0027), "mild" below alpha. This
equals the earlier rule (surprise_index >= 3) whenever
the Normal approximation is used, and avoids mixing an exact p-value
with an approximate distance otherwise.
Shiny app (Conflict module): the “exact prior predictive” option is now on by default; unchecking it uses the Normal approximation throughout.
plot_sensitivity() and plot_tornado() gain
an interactive argument. The default (FALSE)
returns a ggplot object exactly as before;
interactive = TRUE returns a plotly object with readable
tooltips (rounded values and parameter labels instead of raw column
names) and, for the tornado plot, an explicit title and subtitle.Sensitivity module: plots are interactive with formatted tooltips, the range sliders no longer show tick marks, a note is shown when the prior is a mixture (the analysis uses a moment-matched working prior), and the “Analysis type” and outcome buttons clearly distinguish the selected option.
Dark mode: plotly plots are now recoloured natively instead of through a CSS colour-inversion filter, so plot colours and hover labels are correct and plots no longer flash light after an analysis is run.
prior_conflict() gains an exact
argument (default FALSE, matching prior behaviour exactly).
exact = TRUE replaces the Normal approximation to the prior
and to the data likelihood – used by default for box_pvalue
and the s_value derived from it – with the exact prior
predictive distribution, for the prior family/data-type combinations
where one has a closed form: Beta-Binomial (a Beta prior against
type = "binary" data) and Gamma-Poisson/Negative Binomial
(a Gamma prior against type = "poisson" or
type = "survival" data – both reduce to the same
events/exposure model, so one exact check covers both). The
Normal-Normal case (type = "continuous" against a Normal
prior) was already exact and needs no separate path. For prior
family/data-type combinations with no closed-form check implemented,
exact = TRUE falls back to the Normal approximation with a
message rather than erroring; $box_pvalue_method on the
returned object reports which was used (“exact” or “normal_approx”).
surprise_index, kl_prior_likelihood, and
overlap are unaffected by exact, since they
are defined with respect to the Normal-approximated prior and likelihood
regardless.
Added map_prior(): derives a
meta-analytic-predictive (MAP) prior from historical trial summaries via
a random-effects meta-analysis, with an explicit prior on the
between-trial heterogeneity parameter tau (Schmidli et al., 2014). The
meta-analysis engine is implemented entirely in base R: the posterior of
mu given tau has a closed form, so the only numerical step is a
one-dimensional integral over tau
(stats::integrate()/stats::optimize()/stats::uniroot(),
all base
R) – bayprior has no dependency, direct or optional, on
any external meta-analysis package for this. outcome_type
presets select the default tau prior from Roever et al. (2021)’s
outcome-specific recommendations, distinguishing a single-arm log-odds
case (e.g. historical control response rates – the typical MAP use case)
from a two-arm log-odds ratio case, which get different default
heterogeneity-prior scales despite both being “log-odds” in casual
speech.
Added historical_effect_sizes(), a thin wrapper
around metafor::escalc() (Suggests, not
Imports) that converts raw per-trial summary statistics
(event counts, arm means/SDs, …) into the y/se
inputs map_prior() expects, across all six supported
outcome_types.
Added a Shiny module for map_prior(), “MAP Prior
(Historical)”, as its own top-level sidebar item alongside “Prior
Elicitation” and “Expert Pooling” – reflecting that MAP derivation is an
alternative way to construct a base informative prior, not a robustness
adjustment applied to one (Robust Mixture, Sceptical, and Power Prior
all take an existing base prior as input; MAP produces one). Its
step-completion indicator is tracked independently of Robust Priors’
own.
prior_conflict() now also reports
s_value, the surprisal -log2(box_pvalue) in
bits – an exploratory, continuous companion to the existing Box p-value
/ thresholded conflict_severity classification, since
prior-data conflict checks are typically not powered for a binary
accept/reject decision. Reported alongside (not instead of) the existing
diagnostics in the console print() method, the Shiny
Conflict Diagnostics tab, and the prior_report() regulatory
report.
Restyled the app around the package palette (navy and emerald).
The stylesheet custom.css shipped with the app but was
never linked, so it had no effect; it is now loaded, with dark-mode
variants. Result tiles, box headers, alerts, and notifications use
colour for status only, and text colours were checked for WCAG AA
contrast.
The Welcome page workflow diagram is now live: steps link to their panels and show completed steps. A “Start here” panel (elicit, derive from historical trials, or load the TRIAL-001 example) becomes “Next steps” once a prior exists. The Report step is marked complete after a report is generated and reopens when the prior or any analysis changes.
Fitting a prior (Parametric, Roulette, or MAP) while a pooled consensus prior is active now asks whether to replace the consensus or keep it, instead of leaving downstream steps on the old consensus without notice.
Fixed a JavaScript error in the theme-toggle script, which ran before the page body existed and stopped the rest of the script from loading. The Auto / Dark / Light button label now shows correctly on load.
sensitivity_grid() and
sensitivity_cri() now moment-match a mixture prior’s actual
pooled mean and SD to a working prior, instead of silently analyzing
only the dominant component by weight. Under equal or near-equal expert
weights, this previously meant the sensitivity grid could reflect a
single expert’s prior without any indication that other experts’ input
had been dropped. A message now reports the working prior used; a
warning is issued if the mixture’s family cannot be moment-matched from
mean/SD (Exponential, Weibull), in which case the dominant-component
fallback is used and clearly identified.
Fixed the pairwise Bhattacharyya agreement coefficient
(aggregate_experts()) silently reporting near-total
disagreement for any pair of experts using Lognormal, Exponential, or
Weibull priors (or Gamma, via a related integration-range issue),
regardless of how similar their actual priors were. This always
triggered the “substantial expert disagreement” warning for these
families.
Fixed plot() on a bayprior_conflict
object and plot_prior_likelihood() silently clipping the
likelihood curve when it falls substantially outside the prior’s own
range. Both functions previously built their plotting grid from the
prior’s range alone; since these plots exist specifically to visualise
prior-vs-data agreement or conflict, a genuinely severe conflict (the
case the plot is meant to surface) could mean the likelihood curve was
dropped from the visible range entirely. The grid now spans both the
prior’s and the likelihood’s range.
Fixed plot() on a bayprior_conflict
object erroring for any prior other than Beta (Normal, Gamma, Lognormal,
Exponential, Weibull), and the plotted x-axis range being incorrectly
clamped to [0, 1] regardless of the prior’s actual support.
Fixed plot_prior_likelihood()’s likelihood curve for
Poisson/count and survival data types being centred at the raw event
count rather than the event rate, and referencing an SD field that is
never collected for these data types (only continuous data has one).
This affected the “Prior-Likelihood-Posterior overlay” panel in the
Shiny app’s Conflict Diagnostics tab for any Poisson or survival
analysis.
Fixed .conjugate_update() aborting for any
prior/data-type pairing with no exact conjugate formula (e.g. a Beta
prior with continuous data), which contradicted the compatibility
warning shown elsewhere in the package promising the analysis would
proceed via a Normal approximation. Now falls back to a
Normal-approximation posterior instead, consistent with that promise.
This also fixes the Shiny app’s “Prior-Likelihood-Posterior overlay”
panel erroring (“Could not update any mixture component with the
supplied data”) for a pooled mixture prior with any component/data-type
pairing lacking an exact conjugate update.
Removed a duplicate, dead definition of
sensitivity_cri() that had fallen out of sync with the
version actually in use; the live version had the same mixture-handling
issue described above, now fixed.
Fixed a crash (argument is of length zero) in
mixture-handling logic when called on a logarithmically-pooled prior,
whose fit_summary$sd is NULL by
design.
sensitivity_grid()’s Shiny UI parameter-range
defaults now use the same working-prior logic as the underlying
function, so the interface’s suggested ranges no longer risk diverging
from what is actually analyzed.
Corrected the documented meaning of null_value for
sceptical_prior(..., family = "lognormal"): it must be
supplied on the log scale (e.g. 0 for a null hazard ratio of 1), not the
natural scale implied by the previous generic parameter
documentation.
Fixed conflict_mahalanobis()’s documented return
field names (mahal_d2, p_value), which did not
match the object’s actual field names (mahal_D2,
pvalue).
Corrected the title of the FDA’s 2026 draft guidance as cited throughout the package documentation, vignettes, Shiny app, and regulatory report template. The guidance is titled “Use of Bayesian Methodology in Clinical Trials of Drug and Biological Products.”
Updated the EMA reference in the regulatory report, Shiny app, and vignettes to reflect the agency’s current published position: a 2026 concept paper proposing the development of guidance on Bayesian methods (EMA/CHMP/1813/2026), with the full reflection paper expected in 2027-2028.
Corrected the FDA guidance section references in the
regulatory-reporting vignette’s compliance-checklist table
to match the guidance document’s structure (Sections V.A, V.C, V.D, V.F,
and VIII.B).
Clarified attribution of the sceptical prior and the enthusiastic/ sceptical sensitivity pairing to Spiegelhalter & Freedman (1994).
Removed an inaccurate cross-reference to ICH E9(R1) in the
sensitivity-analysis vignette; ICH E9(R1) addresses
estimands and intercurrent events rather than prior
specification.
Aligned the package website overview (index.qmd)
with the README’s description of related packages.
print.bayprior(),
print.bayprior_conflict(),
print.bayprior_conflict_mv(), and
print.bayprior_power_prior() producing no output when
called inside rmarkdown::render(), knitr vignettes, or any
non-interactive R subprocess (e.g. callr::r()). Output now
renders correctly in both interactive and non-interactive sessions
(#40).Wrapped software name in single quotes in DESCRIPTION: ‘Shiny’, per CRAN policy on package and software name formatting.
Added six methodology references to DESCRIPTION in CRAN-required format (authors, year, doi/ISBN): O’Hagan et al. (2006), Box (1980), Oakley and O’Hagan (2010), Schmidli et al. (2014), Ibrahim and Chen (2000), Spiegelhalter et al. (1994).
Added \value documentation tag to four exported
print methods: print.bayprior(),
print.bayprior_conflict(),
print.bayprior_conflict_mv(), and
print.bayprior_power_prior(). Each documents that the
function returns the input object invisibly, called for its side effect
of printing a formatted summary.
Restored par() settings after use in the
robust-priors vignette. The par(oldpar) call was
incorrectly placed before the for loop and
legend(). Moved to after all plotting operations, per CRAN
policy.
Removed LICENSE file and | file LICENSE
from DESCRIPTION. The GPL-3 licence does not require an additional
file.
Updated inst/WORDLIST: added biom,
doi, and ss – fragments extracted by the spell
checker from DOI strings in the DESCRIPTION references field.
.Rbuildignore pattern for
cran-comments.md. The pattern
^^cran-comments\\.md$$ (double caret, double dollar) never
matched the file, so it was included in every previous tarball. Fixed to
the correct regex ^cran-comments\.md$ and verified absent
from the v0.2.11 tarball.Switched Language from en-US to
en-GB in DESCRIPTION to correctly reflect the British
English used throughout the package (sceptical, colour, behaviour,
normalised etc.). British spellings are now handled by the en-GB
dictionary and no longer need to be in
inst/WORDLIST.
Replaced “Kullback-Leibler divergence” in DESCRIPTION with
“information divergence”. Kullback and Leibler are proper nouns not in
any standard dictionary and cannot be resolved via
inst/WORDLIST for CRAN’s DESCRIPTION spell check.
Updated inst/WORDLIST: added HR, Inf, Var, heatmaps,
prior’s, R’s, VignetteBuilder, Poisson, etc. Removed British spellings
now covered by en-GB dictionary.
inst/WORDLIST. These
are proper nouns (the Kullback-Leibler divergence is named after
statisticians Solomon Kullback and Richard Leibler) and are standard
terminology in Bayesian statistics. Flagged as possibly misspelled by
CRAN’s automated pre-check in v0.2.8.Resolved the persistent “Package has a VignetteBuilder field but
no prebuilt vignette index” NOTE that appeared across v0.2.4–v0.2.7.
Root cause: ^build$ was incorrectly added to
.Rbuildignore, which excluded
build/vignette.rds from the tarball. This file is generated
by R CMD build during vignette processing and is the exact
file R CMD check looks for (tools/R/QC.R). Removing
^build$ from .Rbuildignore resolves the note
permanently.
Revised Description field in DESCRIPTION to remove
all regulatory body references and abbreviations. The description now
describes what the software does. Regulatory context is retained in
vignettes and README.
Resolve EMA in DESCRIPTION. The EMA
acronym (European Medicines Agency) was flagged as a possible
misspelling in the DESCRIPTION file, causing a CRAN resubmission
WARNING.
Updated inst/WORDLIST.
Updated inst/WORDLIST to include all
package-specific terms flagged by the spelling checker, and package
infrastructure terms (golem, shinydashboard, knitr, pandoc). Resolved
spelling.Rout vs spelling.Rout.save mismatch
in tests.
Changed Language field in DESCRIPTION from
en-US to en-GB to reflect the package’s
consistent use of British English spelling throughout documentation and
vignettes (sceptical, colour, behaviour, centred).
Added prebuilt vignette index (inst/doc/index.html)
to resolve the “no prebuilt vignette index” NOTE reported by CRAN’s
automated pre-check on Windows and Debian.
Removed all Unicode characters from R source files that caused LaTeX PDF generation errors on CRAN’s Windows and Debian build servers. Affected characters: U+2014 em dash (replaced with –), U+2212 Unicode minus (replaced with -), U+221E infinity (replaced with Inf), U+2019/U+2018 curly quotes (replaced with ’), and others. All replacements made in comments and roxygen documentation only – no functional code changed.
Added inst/WORDLIST declaring package-specific
acronyms (EMA, FDA, CrI, MAP, Mahalanobis, Bhattacharyya, SHELF, PFS,
PK) that were flagged as possibly misspelled in the DESCRIPTION
file.
Rebuilt prebuilt vignette index via
devtools::build_vignettes() to resolve the “no prebuilt
vignette index” NOTE.
robust_prior() – added @details section
explaining that the vague component is always Normal, making
cross-family mixtures (e.g. Beta + Normal) structurally inevitable for
non-Normal informative priors.
elicit_mixture() – added @details
section documenting the numerical density approximation behaviour at the
lower level where mixtures are first constructed.
Robust Mixture density plot warning redirected from console to
UI. The “Components have different distribution families. Mixture
densities computed numerically.” warning is now surfaced as an amber
showNotification() in the Shiny app.
Introduced shared$base_prior reactive to eliminate
the self-invalidation loop that wiped sensitivity/robust/power prior
results immediately after they were produced.
Fixed robust mixture compounding SD bug – repeated clicks no longer compound the vague SD exponentially.
tests/testthat/test-robust.R (33 tests).tests/testthat/test-validation.R (10 tests).skip_on_ci() to all shinytest2
Chromote tests.pkgload::load_all() from
app.R.renv::snapshot(type = "explicit") to deployment
workflow.elicit_exponential() – Exponential(rate) prior for
hazard rates and Poisson rate priors. Supports moments, rate, and
quantile methods.
elicit_weibull() – Weibull(shape, scale) prior for
survival times. Supports moments, params, and quantile methods.
"poisson" and "survival" data types in
prior_conflict(), sensitivity_grid(), and
sensitivity_cri().
Density and x-range support for Exponential and Weibull in
plot.bayprior().
Comprehensive validation layer:
.check_prior_data_compat(),
.check_pooling_compat(),
.check_sensitivity_compat(),
.validation_alert().
Sensitivity analysis fully independent of conflict diagnostics.
All Shiny modules reset outputs automatically on input change.
prior_report() blank figures in Word
reports.prior_report() PDF “tikzfill.image.sty not found”
error.mod_conflict_mahal.R stale duplicate definition
of mod_conflict_ui.Fixed prior_report() failing on remote platforms due
to Quarto spawning a subprocess that could not find the
bayprior package.
Fixed mod_sensitivity always using
type = "continuous" regardless of prior family.
Fixed non-ASCII characters in R/app_ui.R causing R
CMD check WARNING.
Fixed prior_report() missing @param
documentation for plot arguments.
Added robust, sceptical, and power prior arguments to
prior_report().
Added bayprior hex logo to Shiny app header and browser favicon.
elicit_beta(),
elicit_normal(), elicit_gamma(),
elicit_lognormal(), elicit_roulette(),
elicit_mixture().aggregate_experts() with Bhattacharyya
diagnostics.prior_conflict(),
conflict_mahalanobis().sensitivity_grid(),
sensitivity_cri().robust_prior(),
sceptical_prior(),
calibrate_power_prior().prior_report() (HTML, PDF, Word).run_app().