Uses simple Bayesian conjugate prior update rules to calculate the following metrics for various marketing objectives:
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.
To add a new posterior distribution you must complete the following:
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.sample_from_posterior()
A new row must be added to the internal data object distribution_column_mapping
.
use_data(new_tibble, internal = TRUE, overwrite = TRUE)
and it will be saved as sysdata.rda
in the package for internal use.Create a PR for review.
update_rules
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.
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