Collection of data sets from various assessments that can be used to 
    evaluate psychometric models. These data sets have been analyzed in the
    following papers that introduced new methodology as part of the application section:
    Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023) <doi:10.1111/bmsp.12307>,
    Culpepper, S. A., & Balamuta, J. J. (2021) <doi:10.1080/00273171.2021.1985949>,
    Yinghan Chen et al. (2021) <doi:10.1007/s11336-021-09750-9>,
    Yinyin Chen et al. (2020) <doi:10.1007/s11336-019-09693-2>,
    Culpepper, S. A. (2019a) <doi:10.1007/s11336-019-09683-4>,
    Culpepper, S. A. (2019b) <doi:10.1007/s11336-018-9643-8>,
    Culpepper, S. A., & Chen, Y. (2019) <doi:10.3102/1076998618791306>,
    Culpepper, S. A., & Balamuta, J. J. (2017) <doi:10.1007/s11336-015-9484-7>,
    and Culpepper, S. A. (2015) <doi:10.3102/1076998615595403>.

Implementations of (1) mutual dependence measures and mutual independence tests in 
    Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532>;
    (2) independent component analysis methods based on mutual dependence measures in 
    Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532> 
    and Pfister, N., et al. (2018) <doi:10.1111/rssb.12235>;
    (3) conditional mean dependence measures and conditional mean independence tests in 
    Shao, X., and Zhang, J. (2014) <doi:10.1080/01621459.2014.887012>,
    Park, T., et al. (2015) <doi:10.1214/15-EJS1047>,
    and Lee, C. E., and Shao, X. (2017) <doi:10.1080/01621459.2016.1240083>.

Models integrate environmental DNA (eDNA) detection data and traditional survey data to jointly estimate species catch rate (see package vignette: <https://bookdown.org/abigailkeller/eDNAjoint_vignette/>). Models can be used with count data via traditional survey methods (i.e., trapping, electrofishing, visual) and replicated eDNA detection/nondetection data via polymerase chain reaction (i.e., PCR or qPCR) from multiple survey locations. Estimated parameters include probability of a false positive eDNA detection, a site-level covariates that scale the sensitivity of eDNA surveys relative to traditional surveys, and catchability coefficients for traditional gear types. Models are implemented with a Bayesian framework (Markov chain Monte Carlo) using the 'Stan' probabilistic programming language.