  Booming in business and a staple analysis in medical trials, the A/B test
assesses the effect of an intervention or treatment by comparing its success
rate with that of a control condition. Across many practical applications, it
is desirable that (1) evidence can be obtained in favor of the null hypothesis
that the treatment is ineffective; (2) evidence can be monitored as the data
accumulate; (3) expert prior knowledge can be taken into account. Most existing
approaches do not fulfill these desiderata. Here we describe a Bayesian A/B
procedure based on Kass and Vaidyanathan (1992) that allows one to monitor the
evidence for the hypotheses that the treatment has either a positive effect, a
negative effect, or, crucially, no effect. Furthermore, this approach enables
one to incorporate expert knowledge about the relative prior plausibility of
the rival hypotheses and about the expected size of the effect, given that it
is non-zero. To facilitate the wider adoption of this Bayesian procedure we
developed the abtest package in R. We illustrate the package options and the
associated statistical results with a fictitious business example and a real
data medical example.
