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Hypothesis testing is a central statistical method in psychology and thecognitive sciences. However, the problems of null hypothesis significancetesting (NHST) and p-values have been debated widely, but few attractivealternatives exist. This article introduces the fbst R package, whichimplements the Full Bayesian Significance Test (FBST) to test a sharp nullhypothesis against its alternative via the e-value. The statistical theory ofthe FBST has been introduced by Pereira et al. (1999) more than two decades agoand since then, the FBST has shown to be a Bayesian alternative to NHST andp-values with both theoretical and practical highly appealing properties. Thealgorithm provided in the fbst package is applicable to any Bayesian model aslong as the posterior distribution can be obtained at least numerically. Thecore function of the package provides the Bayesian evidence against the nullhypothesis, the e-value. Additionally, p-values based on asymptotic argumentscan be computed and rich visualisations for communication and interpretation ofthe results can be produced. Three examples of frequently used statisticalprocedures in the cognitive sciences are given in this paper which demonstratehow to apply the FBST in practice using the fbst package. Based on the successof the FBST in statistical science, the fbst package should be of interest to abroad range of researchers in psychology and the cognitive sciences andhopefully will encourage researchers to consider the FBST as a possiblealternative when conducting hypothesis tests of a sharp null hypothesis.

fbst: An R package for the Full Bayesian Significance Test for testing a sharp null hypothesis against its alternative via the e-value