BFpack: Flexible Bayes Factor Testing of Scientific Theories in R

Abstract

There have been considerable methodological developments of Bayes factors for hypothesis testing in the social and behavioral sciences, and related fields. This development is due to the flexibility of the Bayes factor for testing multiple hypotheses simultaneously, the ability to test complex hypotheses involving equality as well as order constraints on the parameters of interest, and the interpretability of the outcome as the weight of evidence provided by the data in support of competing scientific theories. The available software tools for Bayesian hypothesis testing are still limited however. In this paper we present a new R package called BFpack that contains functions for Bayes factor hypothesis testing for the many common testing problems. The software includes novel tools for (i) Bayesian exploratory testing (e.g., zero vs positive vs negative effects), (ii) Bayesian confirmatory testing (competing hypotheses with equality and/or order constraints), (iii) common statistical analyses, such as linear regression, generalized linear models, (multivariate) analysis of (co)variance, correlation analysis, and random intercept models, (iv) using default priors, and (v) while allowing data to contain missing observations that are missing at random.

A review of applications of the Bayes factor in psychological research.

“…Additionally, the OSF folder “Null Hypotheses” provides vignettes illustrating Bayesian updating (repeated addition of data and recomputation of the Bayes factor) and sample size determination (loosely spoken, Bayesian power analysis). The main software resources for the evaluation of null hypotheses are the R package BayesFactor (Morey & Rouder, 2018) which can be used for t tests, various types of ANOVAs, multiple regression, and contingency tables; the R packages bain (Gu et al, 2019) and BFpack (Mulder et al, 2019), which can handle the evaluation of null hypotheses in the context of virtually any statistical model; and JASP (JASP Team, 2020), which offers an easy-to-use GUI for the Bayesian evaluation of many standard models in psychology (based on packages such as bain and BayesFactor).…”

“…On the OSF website corresponding to this article, additional examples illustrate the evaluation of interval hypotheses for equivalence testing with complementary hypotheses and for the application of noninferiority testing in the context of financial statement audits. The main software resources for the evaluation of interval hypotheses are the R packages baymedr (Linde & van Ravenzwaaij, 2021), BayesFactor (Morey & Rouder, 2018), bain (Gu et al, 2019; partly also implemented in JASP which offers an easy-to-use GUI; JASP Team, 2020), and BFpack (Mulder et al, 2019).…”

A Review of Bayesian Hypothesis Testing and Its Practical Implementations

“…However, the issue is being mitigated by the growth of computational power and the availability of open-source statistical tools for this computation. Examples of these tools are BayesFactor , brms , and BFpack R packages [ 23 , 24 , 25 ]; and JASP [ 26 ] software. In Section 4 , we illustrate the required R scripting for a number of examples widely used in data analysis.…”

Software for Bayesian Statistics

“…Gronau, Raj K. N., and Wagenmakers (2021) show how to perform Bayesian inference for A/B tests using R package abtest, which allows for the incorporation of expert knowledge in the priors. Mulder et al (2021) describe a general framework for testing hypotheses using Bayes factors for many different types of models.…”