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).