statConfR: An R Package for Static Models of Decision Confidence and Metacognition

We present the statConfR package for R, which allows researchers to conveniently fit and compare nine different static models of decision confidence applicable to binary discrimination tasks with confidence ratings: the signal detection rating model (Green & Swets, 1966), the Gaussian noise model (Maniscalco & Lau, 2016), the independent Gaussian model (Rausch & Zehetleitner, 2017a), the weighted evidence and visibility model (Rausch, Hellmann, et al., 2018), the lognormal noise model (Shekhar & Rahnev, 2021), the lognormal weighted evidence and visibility model (Shekhar & Rahnev, 2023), the independent truncated Gaussian model (Rausch et al., 2023) based on the meta-d′/d′ (Maniscalco & Lau, 2012, 2014), and the independent truncated Gaussian model based on the Hmetad method (Fleming, 2017). In addition, the statConfR package provides functions for estimating meta-d′/d′, the most widely-used measure of metacognitive efficiency, allowing both Maniscalco and Lau (2012)'s and Fleming (2017)'s model specification.