r4lineups: Statistical Inference on Lineup Fairness

#'Effective Size (across a dataframe)
#'
#'Function for computing Effective Size (Tredoux, 1998) on
#'lineups contained as columns in a df, usually from a bootstrapped sample
#'@param lineup_boot_df A dataframe containing bootstrapped samples of lineup data
#'@param k Number of members in lineup. Must be specified by user (scalar).
#'@details This function computes effective size for k lineups simultaneously.
#'@return A vector of effective size calculations for each lineup in bootstrapped df
#'@references Malpass, R. S. (1981). Effective size and defendant bias in
#'            eyewitness identification lineups. \emph{Law and Human Behavior, 5}(4), 299-309.
#'
#'            Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup
#'            construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read,
#'            & M. P. Toglia (Eds.), \emph{Handbook of Eyewitness Psychology, Vol. 2: Memory for
#'            people} (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.
#'
#'            Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness.
#'            \emph{Law and Human Behavior, 22}(2), 217-237.
#'
#'            Tredoux, C. (1999). Statistical considerations when determining measures of
#'            lineup size and lineup bias. \emph{Applied Cognitive Psychology}, 13, S9-S26.
#'
#'            Wells, G. L.,Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for
#'            empirically assessing the fairness of a lineup. \emph{Law and Human Behavior,
#'            3}(4), 285-293.
#'@examples
#'#Data:
#'lineup_vec <- round(runif(100,1,6))
#'bootdf <- gen_boot_samples(lineup_vec, 1000)
#'
#'#Call:
#'esize_vec <- gen_esize_m(bootdf, 6)
#'
#'@export
#'@importFrom magrittr %>%
#'@importFrom purrr map map_dbl
#'

gen_esize_m <- function (lineup_boot_df, k){
  table_boot_df <- map(lineup_boot_df,~table(.))
    map_dbl(table_boot_df, ~ esize_m(., k))
}