Nothing

```
#' Apply monotonic binning to all vectors in dataframe
#'
#' The function \code{batch_bin} applies multiple binning algorithms in
#' batch to each vector in the dataframe.
#'
#' @param y A numeric vector with 0/1 binary values.
#' @param xs A dataframe with numeric vectors to discretize.
#' @param method A integer from 1 to 7 referring to implementations below:
#' 1. Implementation of iso_bin() 2. Implementation of qtl_bin()
#' 3. Implementation of bad_bin() 4. Implementation of rng_bin()
#' 5. Implementation of gbm_bin() 6. Implementation of kmn_bin()
#' 7. Implementation of arb_bin()
#'
#' @return A list of binning outcomes with 2 dataframes:
#' bin_sum: A dataframe of binning summary.
#' bin_out: A list of binning output from binning functions,
#' e.g. qtl_bin().
#'
#' @examples
#' data(hmeq)
#' batch_bin(hmeq$BAD, hmeq[, c('DEROG', 'DELINQ')])
batch_bin <- function(y, xs, method = 1) {
bin_fn <- switch(method,
"1" = iso_bin, "2" = qtl_bin, "3" = bad_bin, "4" = rng_bin,
"5" = gbm_bin, "6" = kmn_bin, "7" = arb_bin)
bin_out <- lapply(xs, function(x) bin_fn(x, y))
bin_sum <- Reduce(rbind,
lapply(names(bin_out),
function(n) data.frame(var = n,
nbin = nrow(bin_out[[n]]$tbl),
freq = sum(bin_out[[n]]$tbl$freq),
bads = sum(bin_out[[n]]$tbl$bads),
miss = sum(bin_out[[n]]$tbl$miss),
iv = sum(bin_out[[n]]$tbl$iv),
ks = max(bin_out[[n]]$tbl$ks))))
return(list(bin_sum = bin_sum, bin_out = bin_out))
}
```

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