# grizbayr

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

### Description:

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

- Win Probability of each option
- Value Remaining in the Test
- 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.

## Examples

See the `intro`

vignette for examples to get started.

## Marketing objectives supported:

- Conversion Rate
- Response Rate
- Click Through Rate (CTR)
- Revenue Per Session
- Multi Revenue Per Session
- Cost Per Activation (CPA)
- Total Contribution Margin (CM)
- CM Per Click
- Cost Per Click (CPC)
- Session Duration (seconds)
- Page Views Per Session

## Contributing

### New Posterior Distributions

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

- 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.
- This function (and the name) must be added to the switch statement in
`sample_from_posterior()`

- A new row must be added to the internal data object
`distribution_column_mapping`

.
- Select this object from the package
- Add a new row with a 1 for every column that is required for this distribution (this is for data validation and clear alerting for the end user)
- Save the updated tibble object using
`use_data(new_tibble, internal = TRUE, overwrite = TRUE)`

and it will be saved as `sysdata.rda`

in the package for internal use.
- Update the intro.Rmd markdown table to include which columns are required for your function.

- Create a PR for review.

### New Features Ideas (TODO)

- High Density Credible Intervals with each option
- Conjugate Prior Update Rules vignette deriving each marketing objective
`update_rules`

#### 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.