uSDT 0.1.0

First release.

Data preparation and descriptive estimates

• 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.

Model fitting and inference

• 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.

Visualization and reliability

• 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.

Example data

• vadillo_awareness and vadillo_cuing provide trial-level data from Experiment 2 of Vadillo, Malejka and Shanks (2025).