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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()
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.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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.