bain is an abbreviation for BAyesian INformative hypothesis evaluation. It uses the Bayes factor to evaluate hypotheses in a wide variety of statistical models. One example are the hypotheses H1: m1 = m2 = m3, H2: m1 > m2 > m3, and Hu: m1, m2, m3 (no constraints) where m1, m2, and m3 denote the means in an ANOVA model. Another example is the hypothesis H1: b1 > 0, b2 > 0, b1 > b2 and its complement Hc: not H1, where b1 and b2 denote standardized regression coefficients.
bain was developed and is being maintained by:
Xin Gu – Department of Educational Psychology – East China Normal UniversityGuXin57@hotmail.com
Herbert Hoijtink – Department of Methodology and Statistics – Utrecht University – H.Hoijtink@uu.nl
Joris Mulder – Department of Methodology and Statistics – Tilburg University – J.Mulder3@uvt.nl
Caspar van Lissa – Department of Methodology and Statistics – Utrecht University – C.J.vanLissa@uu.nl
The following persons have contributed to the further development bain: Marlyne Bosman and Camiel van Zundert
bain is licensed under the GNU General Public License Version >=3. A new version of bain with user friendly hypothesis specification is under development. It will be released in March. Using RStudio it will become downloadable from CRAN or GitHub. It is a beta version, that is, there may still be errors and bugs in the package. Let us know if you find one.
CLICK HERE to obtain all Bain versions, corresponding examples, and installation instructions released prior to the bain version that will become downloadable from CRAN or GitHub. The latest version was Bain-0.1.2
CLICK HERE to obtain BFTutorial.pdf, BFTutorial.R with corresponding data sets, and the Examples.R file from the help documentation corresponding to the bain version that will become downloadable from CRAN or GitHub. Note that the Examples.R file contains all the examples that were previously posted on the website.
Bosman, M. and Hoijtink, H. (unpublished). Robust Bayes factors for Bayesian Anova: overcoming overcoming adverse effect of non-normality and outliers. CLICK HERE to obtain this paper.
Gu, X., Mulder, J., and Hoijtink, H. (2018). Approximate adjusted fractional Bayes factors: A general method for testing informative hypotheses. British Journal of Mathematical and Statistical Psychology, 71, 229-261. DOI: 10.1111/bmsp.12110 CLICK HERE to obtain this paper.
Gu, X., Hoijtink, H., Mulder, J., and Rosseel, Y. (unpublished). Bain: A program for Bayesian testing of order constrained hypotheses in structural equation models. CLICK HERE to obtain this paper, CLICK HERE for the corresponding research archive (constructed for Bain-0.1.2).
Hoijtink, H., Mulder, J., van Lissa, C., and Gu, X. (2018). A tutorial on testing hypotheses using the Bayes factor. Psychological Methods. DOI: 10.1037/met0000201 CLICK HERE to obtain the paper.
Hoijtink, H., Gu, X., and Mulder, J. (2018). Bayesian evaluation of informative hypotheses for multiple populations. British Journal of Mathematical and Statistical Psychology. DOI: 10.1111/bmsp.12145 CLICK HERE to obtain this paper.
Hoijtink, H., Gu, X., Mulder, J., and Rosseel, Y. (in press). Computing Bayes Factors from Data with Missing Values. Psychological Methods. DOI: 10.1037/met0000187 CLICK HERE to obtain the paper
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