R/bayesAB.R

In bayesAB: Fast Bayesian Methods for AB Testing

#' bayesAB: Fast Bayesian Methods for A/B Testing
#'
#' bayesAB provides a suite of functions that allow the user to analyze A/B test
#' data in a Bayesian framework. bayesAB is intended to be a drop-in replacement for
#' common frequentist hypothesis test such as the t-test and chi-sq test.
#'
#' Bayesian methods provide several benefits over frequentist methods in the context of
#' A/B tests - namely in interpretability. Instead of p-values you get direct
#' probabilities on whether A is better than B (and by how much). Instead of point estimates
#' your posterior distributions are parametrized random variables which can be summarized
#' any number of ways. Bayesian tests are also immune to 'peeking' and are thus valid whenever
#' a test is stopped.
#'
#' The general bayesAB workflow is as follows:
#' \itemize{
#'    \item  Decide how you want to parametrize your data (Poisson for counts of email submissions, Bernoulli for CTR on an ad, etc.)
#'    \item  Use our helper functions to decide on priors for your data (\link{bayesTest}, \link{plotDistributions})
#'    \item  Fit a \link{bayesTest} object
#'        \itemize{ \item Optional: Use \link{combine} to munge together several \link{bayesTest} objects together for an arbitrary / non-analytical target distribution}
#'    \item  \code{print}, \link{plot.bayesTest}, and \link{summary.bayesTest} to interpret your results
#'        \itemize{ \item Determine whether to stop your test early given the Posterior Expected Loss in \code{summary} output}}
#'
#' Optionally, use \link{banditize} and/or \link{deployBandit} to turn a pre-calculated (or empty) bayesTest into a multi-armed bandit that can serve
#' recipe recommendations and adapt as new data comes in.
#'
#' Note, while bayesAB was designed to exploit data related to A/B/etc tests, you can use the package to conduct Bayesian analysis on
#' virtually any vector of data, as long as it can be parametrized by the available functions.
#'
#' To learn more about bayesAB, start with the vignettes:
#' \code{browseVignettes(package = "bayesAB")}
#'
#' @docType package
#' @name bayesAB
#' @importFrom graphics par
#' @importFrom methods is
#' @importFrom stats dbeta dnorm qgamma quantile rbeta rgamma rnorm dgamma dpois qpois runif dlnorm qlnorm pnorm var
#' @importFrom utils tail packageVersion head
#' @importFrom rlang .data
#' @useDynLib bayesAB
#' @importFrom Rcpp evalCpp
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