bayesAB: bayesAB: Fast Bayesian Methods for A/B Testing

In FrankPortman/bayesAB: Fast Bayesian Methods for AB Testing

Description

bayesAB provides a suite of functions that allow the user to analyze A/B test data in a Bayesian framework. bayesAB is intended to be a drop-in replacement for common frequentist hypothesis test such as the t-test and chi-sq test.

Details

Bayesian methods provide several benefits over frequentist methods in the context of A/B tests - namely in interpretability. Instead of p-values you get direct probabilities on whether A is better than B (and by how much). Instead of point estimates your posterior distributions are parametrized random variables which can be summarized any number of ways. Bayesian tests are also immune to 'peeking' and are thus valid whenever a test is stopped.

The general bayesAB workflow is as follows:

• Decide how you want to parametrize your data (Poisson for counts of email submissions, Bernoulli for CTR on an ad, etc.)

• Use our helper functions to decide on priors for your data (bayesTest, plotDistributions)

• Fit a bayesTest object

  • Optional: Use combine to munge together several bayesTest objects together for an arbitrary / non-analytical target distribution

• print, plot.bayesTest, and summary.bayesTest to interpret your results

  • Determine whether to stop your test early given the Posterior Expected Loss in summary output

Optionally, use banditize and/or deployBandit to turn a pre-calculated (or empty) bayesTest into a multi-armed bandit that can serve recipe recommendations and adapt as new data comes in.

Note, while bayesAB was designed to exploit data related to A/B/etc tests, you can use the package to conduct Bayesian analysis on virtually any vector of data, as long as it can be parametrized by the available functions.

To learn more about bayesAB, start with the vignettes: browseVignettes(package = "bayesAB")

FrankPortman/bayesAB documentation built on June 25, 2021, 6:49 p.m.