R/data.R

In ebnm: Solve the Empirical Bayes Normal Means Problem

#' @name wOBA
#'
#' @title 2022 MLB wOBA Data
#'
#' @docType data
#'
#' @description Weighted on-base average (wOBA) for hitters from the
#'   2022 Major League Baseball (MLB) season. Standard errors are
#'   calculated by modeling hitting outcomes as multinomially
#'   distributed and plugging in empirical proportions as the \dQuote{true}
#'   outcome probabilities. To handle small sample sizes, standard
#'   errors are lower bounded by the errors that would be obtained using
#'   league-wide proportions rather than the plug-in estimates.
#'
#' @format
#' \code{wOBA} is a data frame with 688 rows and 6 columns:
#' \describe{
#'
#'   \item{FanGraphsID}{The hitter's FanGraphs identifier.}
#'
#'   \item{Name}{The hitter's name.}
#'   \item{Team}{The hitter's MLB team (given as a three-letter code) or
#'   \code{NA} if the hitter played for multiple teams.}
#'   \item{PA}{The hitter's number of plate appearances over the 2022 season.}
#'   \item{x}{The hitter's wOBA over the 2022 season.}
#'
#'   \item{s}{The standard error for the hitter's wOBA.}
#' }
#'
#' @source <https://fangraphs.com>
#'
#' @keywords data
#'
#' @examples
#' data(wOBA)
#' summary(wOBA)
#'
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