sdtlu

sdtlu provides R functions to estimate signal detection theory (SDT) parameters and compute model-based performance metrics for eyewitness lineup data.

Key Features:

• Data Processing: Handles simultaneous lineups, sequential lineups, and show-ups for analysis of eyewitness identification data.
• SDT Parameter Estimation: Determines best-fitting SDT parameters using closed-form solutions rather than Monte Carlo simulation.
• Performance Measures Calculation: Computes model-based metrics including area under the curve (AUC) and diagnosticity.
• Uncertainty Analysis: Uses bootstrap resampling to estimate uncertainty intervals around SDT parameters and performance measures.
• Comparative Analysis: Compares SDT parameters across different datasets to support comparative studies.
• Base-Rate Estimation: Estimates the base-rate of lineups containing a guilty suspect when suspect guilt is unknown.
• Graphical Outputs: Generates data- and model-based Receiver Operating Characteristic (ROC) curves and visual representations of the underlying SDT model.

Scientific Applications:

• Forensic psychology and legal studies: Enhances understanding and evaluation of eyewitness identification processes using SDT-based analyses.
• Lineup procedure and witness reliability assessment: Facilitates assessment of identification reliability and comparison of lineup methodologies.

Methodology:

Closed-form SDT parameter estimation (no Monte Carlo), bootstrap resampling for uncertainty intervals, computation of model-based AUC and diagnosticity, generation of data- and model-based ROC curves and SDT model visualizations, comparative parameter analyses across datasets, and estimation of lineup base-rates.