BFpack - Flexible Bayes Factor Testing of Scientific Expectations

Implementation of default Bayes factors for testing statistical hypotheses under various statistical models. The package is intended for applied quantitative researchers in the social and behavioral sciences, medical research, and related fields. The Bayes factor tests can be executed for statistical models such as univariate and multivariate normal linear models, correlation analysis, generalized linear models, special cases of linear mixed models, survival models, relational event models. Parameters that can be tested are location parameters (e.g., group means, regression coefficients), variances (e.g., group variances), and measures of association (e.g,. polychoric/polyserial/biserial/tetrachoric/product moments correlations), among others. The statistical underpinnings are described in O'Hagan (1995) <DOI:10.1111/j.2517-6161.1995.tb02017.x>, De Santis and Spezzaferri (2001) <DOI:10.1016/S0378-3758(00)00240-8>, Mulder and Xin (2022) <DOI:10.1080/00273171.2021.1904809>, Mulder and Gelissen (2019) <DOI:10.1080/02664763.2021.1992360>, Mulder (2016) <DOI:10.1016/j.jmp.2014.09.004>, Mulder and Fox (2019) <DOI:10.1214/18-BA1115>, Mulder and Fox (2013) <DOI:10.1007/s11222-011-9295-3>, Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) <DOI:10.1037/met0000116>, Hoijtink, Mulder, van Lissa, and Gu (2018) <DOI:10.1037/met0000201>, Gu, Mulder, and Hoijtink (2018) <DOI:10.1111/bmsp.12110>, Hoijtink, Gu, and Mulder (2018) <DOI:10.1111/bmsp.12145>, and Hoijtink, Gu, Mulder, and Rosseel (2018) <DOI:10.1037/met0000187>. When using the packages, please refer to the package Mulder et al. (2021) <DOI:10.18637/jss.v100.i18> and the relevant methodological papers.