Package index • uSDT

Prepare the data

Build paired task data and dichotomize continuous responses.

usdt_data_tasks() usdt_data_long() print(<usdt_data>): Prepare data for hierarchical SDT models
meyen_split(): Dichotomize response times into binary choices

Estimate descriptive SDT measures

Calculate subject-level SDT quantities without fitting a model.

sdt_moments(): Signal detection measures for each subject

Fit the hierarchical model

Estimate the paired sensitivities with a binomial probit mixed model.

hsdt() summary(<hsdt>) print(<hsdt>): Fit a hierarchical signal detection theory model

Test hypotheses and bootstrap uncertainty

sensitivity_diff() compares mean sensitivities; latent_cor() estimates their latent correlation; latent_regression() estimates the intercept and slope. usdt_tests() collects all three hypotheses. usdt_boot() adds parametric bootstrap inference to a fitted uSDT model.

usdt_tests() sensitivity_diff() latent_cor() latent_regression(): Test the three core hypotheses of a hierarchical SDT model
usdt_boot(): Parametric bootstrap intervals for hierarchical SDT models

Visualize the fit and estimate reliability

Compare observed and fitted estimates and quantify measurement reliability.

plot(<hsdt>): Diagnostic and analytical plots for hierarchical SDT models
usdt_reliability() summary(<usdt_reliability>) print(<usdt_reliability>): Reliability of direct and indirect task measures

Example datasets

Trial-level data from Experiment 2 of Vadillo, Malejka and Shanks (2025).

vadillo_awareness: Awareness data from a probabilistic cuing experiment
vadillo_cuing: Cuing data from a probabilistic cuing experiment