  There has been a tremendous methodological development 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 (i) for Bayesian exploratory testing (null vs positive vs
negative effects), (ii) for Bayesian confirmatory testing (competing hypotheses
with equality and/or order constraints), (iii) for 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.
