\donttest{}.usdt_data_tasks(), task-specific lists,
task_levels, labels, dichotomize
and sensitivity-term arguments. The old names are no longer
accepted."A", with model terms
d_A and c_A. Planning columns use
trials_awareness, trials_influence,
reliability_awareness and
reliability_influence.hsdt(estimation = "bayesian") fits with brms and Stan
and returns the brmsfit in $fit. Both rstan
and cmdstanr are supported. Compiled models are cached. Defaults are
four chains with 1,000 warmup iterations and 5,000 kept draws per chain.
Sampling reports divergences, tree depth, R-hat and effective sample
sizes.usdt_priors() specifies priors for task means, SDs and
correlations. plot() checks their predictive and hypothesis
implications.hsdt(latent = "regression") places the
influence-location prior on the latent intercept instead of the mean.
This changes the induced prior of the mean difference. The intercept
prior also affects inference on the correlation.unequal_variances = TRUE estimates signal SDs for
Bayesian fits.usdt_tests() and the individual hypothesis functions
accept Bayesian hsdt and custom brms fits. They report
posterior means, SDs, central credible intervals and two-sided posterior
sign p-values.usdt_bf() tests point, directional and interval
hypotheses about diff, rho,
intercept and slope. Point nulls use
Savage-Dickey density ratios. Directions and regions compare posterior
with prior odds. Reports give both log Bayes factors. By default,
prior/posterior plots accompany them. Use plot = FALSE to
suppress drawing and plot(bf) to draw later.plot() and usdt_reliability() support
Bayesian fits.usdt_equivalence() accepts frequentist
hsdt and custom glmer fits. Hypotheses use the syntax of
usdt_bf(), with explicit bounds for equivalence (TOST),
non-inferiority, one-sided and minimal-effect tests. Reports include the
interval used for the decision. Bootstrap replicates are used when
available. Unsupported inference is marked not estimable.usdt_population() defines population parameters,
optionally from a frequentist pilot fit. Multiple values create
scenarios. update() changes them. A latent intercept can
replace the influence mean.usdt_power() evaluates designs or grids using a
sampling-distribution approximation. It supports the hypotheses of
usdt_equivalence(). combine = "all" adds joint
rejection power. Non-estimable outcomes count as failures to reject, and
populations in H0 are flagged.usdt_power(method = "simulation") simulates and fits
whole studies, repeating the median split for dichotomized tasks. It
reports Monte Carlo error, Wilson intervals and fitting failures.
summary() adds bias, standard errors, interval widths and
coverage. Results are reproducible across core counts. Unequal-variance
populations can be simulated and analysed with the equal-variance
model.usdt_plan() selects subjects, trials or both to meet a
power or precision target at minimum cost, or maximize power within a
budget. Ranges identify free values. Single numbers fix them. Check a
chosen plan with
usdt_power(plan, method = "simulation").usdt_mdes() finds detectable effects or equivalence
regions. vary selects another population parameter to
vary.plot() supports populations, power curves, plans and
detectable effects. The planning
tutorial illustrates the workflow.usdt_aggregate() counts binary trials by grouping
columns for custom binomial models with covariates.caption = FALSE.nrow = 2 to stack the observed
and latent panels.First release.
usdt_data_tasks() and usdt_data_long()
prepare paired direct and indirect measures and report the column
mappings and preparation diagnostics. Every column, level and format
argument takes either one value for both tasks or one per task as
list(direct = ..., indirect = ...), so the two tasks may
differ in the columns they use, in the values those columns take, and in
the format they arrive in.meyen_split() dichotomizes continuous responses using a
within-subject median pooled across conditions, following Meyen et
al. (2022).sdt_moments() computes subject-level SDT estimates and
optional sampling variances without fitting a hierarchical model.hsdt() fits a binomial probit mixed model with
correlated random sensitivities and returns the three hypothesis
tests.sensitivity_diff(), latent_cor() and
latent_regression() calculate the mean sensitivity
difference, latent correlation and latent regression.
usdt_tests() collects the analytical tests in one
table.usdt_boot() adds parametric bootstrap inference to the
fitted object’s hypothesis table when enough usable replicates are
available.plot() for a fitted hsdt object draws
observed and latent regressions, shrinkage, subject intervals and
model-implied ROC curves.usdt_reliability() estimates task reliability from the
fitted between-subject sensitivity variance and trial-level measurement
variance.vadillo_awareness and vadillo_cuing
provide trial-level data from Experiment 2 of Vadillo, Malejka and
Shanks (2025).