Abstract The modeling of response times using sequential sampling models has a long history. Because choices, confidence judgments, and reaction times are closely linked in perceptual decisions, it seems only natural to simultaneously model these three outcome variables of a decision. In the package dynConfiR, we implemented various sequential sampling models of choice, response time, and decision confidence in R. This paper gives an overview of the package, which provides probability density functions as well as high-level functions for fitting parameters to empirical data, prediction of reaction time and response distributions, and simulation of artificial data sets. We describe the mathematical specifications of the implemented models and provide detailed descriptions of the implemented likelihood functions. In addition, we outline the workflow for applying the model to empirical data step-by-step: data preprocessing, model fitting, model prediction, quantitative model comparison, and visual assessment of model predictions. Finally, we present results from parameter and model recovery analyses and assess the precision of probability density calculations, illustrating the robustness of the implemented computations. Offering intuitive usability and high flexibility, the package is targeted at researchers in the fields of decision-making and confidence and does not require expert-level programming skills.
Hellmann et al. (Fri,) studied this question.