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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).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.