Nothing

```
#' Preprocess a co-elution profile
#'
#' Clean a co-elution/co-fractionation profile by (1) imputing single missing
#' values with the average of neighboring values, (2) replacing missing values
#' with random, near-zero noise, and (3) smoothing with a moving average
#' filter.
#'
#' @param chromatogram a numeric vector corresponding to the chromatogram trace
#' @param impute_NA if true, impute single missing values with the average of
#' neighboring values
#' @param smooth if true, smooth the chromatogram with a moving average filter
#' @param smooth_width width of the moving average filter, in fractions
#' @param noise_floor mean value of the near-zero noise to add
#'
#' @return a cleaned profile
#'
#' @examples
#' data(scott)
#' chrom <- scott[16, ]
#' cleaned <- clean_profile(chrom)
#'
#' @importFrom stats runif setNames
#' @importFrom forecast ma
#'
#' @export
clean_profile <- function(chromatogram, impute_NA = TRUE, smooth = TRUE,
smooth_width = 4, noise_floor = 0.001) {
# copy chromatogram
cleaned <- chromatogram
# impute missing values using mean of neighbors
if (impute_NA) {
impute_neighbors(cleaned)
}
# replace remaining NAs with near-zero noise
nas <- is.na(cleaned) | cleaned == 0
cleaned[nas] <- runif(sum(nas), min = 0, max = noise_floor)
# smooth with a moving average filter
if (smooth) {
smooth_input <- c(runif(smooth_width, min = 0, max = noise_floor),
cleaned,
runif(smooth_width, min = 0, max = noise_floor))
smoothed <- ma(smooth_input, smooth_width)
smoothed <- smoothed[seq(smooth_width + 1, length(smoothed) - smooth_width)]
cleaned <- setNames(smoothed, names(cleaned))
}
return(cleaned)
}
```

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