A Bayesian Inference Package for A|B and Bandit Marketing Tests


Uses simple Bayesian conjugate prior update rules to calculate the following metrics for various marketing objectives:

  1. Win Probability of each option
  2. Value Remaining in the Test
  3. Percent Lift Over the Baseline

This allows a user to implement Bayesian Inference methods when analyzing the results of a split test or Bandit experiment.


See the intro vignette for examples to get started.

Marketing objectives supported:


New Posterior Distributions

To add a new posterior distribution you must complete the following:

  1. Create a new function called sample_...(input_df, priors, n_samples). Use the internal helper functions update_gamma, update_beta, etc. included in this package or you can create a new one.
  2. This function (and the name) must be added to the switch statement in sample_from_posterior()
  3. A new row must be added to the internal data object distribution_column_mapping.
  4. Create a PR for review.

New Features Ideas (TODO)

Package Name

The name is a play on Bayes with an added r (bayesr). The added griz (or Grizzly Bear) creates a unique name that is searchable due to too many similarly named packages.