banditize: Create a multi-armed Bayesian bandit object.
In FrankPortman/bayesAB: Fast Bayesian Methods for AB Testing

Description Usage Arguments Details Value Examples

Fit a multi-armed bandit object based on a bayesTest which can serve recommendations and adapt to new data.

1	banditize(bT, param, higher_is_better = TRUE)

`bT`	a bayesTest object
`param`	which model parameter (posterior) to evaluate; defaults to first param
`higher_is_better`	is a higher value of 'param' equivalent to a better choice?

banditize is an 'object-oriented' implementation of multi-armed bandits in bayesAB. It is useful in conjunction with a Shiny app or Plumber deployment. The object itself is mutable and can adapt/learn from new data without having to re-assign the variable.

Comes with 5 methods:

serveRecipe(): serves a recipe to show your user based on samples from both posteriors.
setResults(results): set results for one or more recipes for one or more instances of feedback. Used to update bandit.
getBayesTest(): returns most updated bayesTest object.
getOriginalTest(): returns original bayesTest object without any updates.
getUpdates(): returns a summarized version of all updates this bandit has processed.

A bayesBandit object.

A_binom <- rbinom(100, 1, .5)
B_binom <- rbinom(100, 1, .6)

AB1 <- bayesTest(A_binom, B_binom, priors = c('alpha' = 1, 'beta' = 1), distribution = 'bernoulli')

binomialBandit <- banditize(AB1)
binomialBandit$serveRecipe()
binomialBandit$setResults(list('A' = c(1, 0, 1, 0, 0), 'B' = c(0, 0, 0, 0, 1)))