                        Changes in version 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).
