diff --git a/MFAssignR/R/RecalFunctions.R b/MFAssignR/R/RecalFunctions.R
index 811a82b..766f1cc 100644
--- a/MFAssignR/R/RecalFunctions.R
+++ b/MFAssignR/R/RecalFunctions.R
@@ -1,3 +1,104 @@
+#' Plot Spectrum Function
+#'
+#' This function generates a mass spectrum plot using ggplot2.
+#'
+#' @param plotpeak A data frame containing information for the first set of segments.
+#' @param RecalPlot A data frame containing information for the second set of segments.
+#'
+#' @return A ggplot object representing the mass spectrum plot.
+#'
+#' @examples
+#' plot_spectrum(plotpeak_data, RecalPlot_data)
+#'
+plot_spectrum <- function(plotpeak, RecalPlot) {
+  ggplot() +
+    geom_segment(data = plotpeak, size = 0.7, aes_string(x = "mass", xend = "mass", y = 0, yend = "Abundance"), alpha = 0.3, color = "grey57") +
+    geom_segment(data = RecalPlot, size = 1.2, aes_string(x = "Exp_mass", xend = "Exp_mass", y = 0, yend = "Abundance"), color = "blue") +
+    theme_bw() +
+    labs(x = "Ion Mass", y = "Abundance", title = "Assignment Mass Spectrum", color = "Series") +
+    theme(
+      axis.title = element_text(size = 15, face = "bold"),
+      strip.text = element_text(size = 15, face = "bold"),
+      axis.text = element_text(size = 15, face = "bold"),
+      legend.title = element_text(face = "bold", size = 15),
+      legend.text = element_text(face = "bold", size = 15),
+      panel.grid.minor.x = element_blank(),
+      panel.grid.major.x = element_blank(),
+      strip.background = element_blank(),
+      plot.title = element_text(size = 16, face = "bold")
+    )
+}
+
+#' Process Mass List
+#'
+#' This function processes a data frame containing mass-related information.
+#'
+#' @param data A data frame containing at least the "Exp_mass" column.
+#' @return The input data frame with additional calculated columns.
+#'
+#' @details
+#' The function calculates the following columns:
+#'   - NM: Rounded Exp_mass
+#'   - KM_O: Exp_mass * (16 / 15.9949146223)
+#'   - KMD_O: Rounded NM - KM_O
+#'   - z_O: Rounded Exp_mass %% 16 - 16
+#'   - KM_H2: Exp_mass * (2 / 2.01565)
+#'   - KMD_H2: Rounded NM - KM_H2
+#'   - z_H2: Rounded Exp_mass %% 2 - 2
+#'
+#' @examples
+#' data <- data.frame(Exp_mass = c(100, 150, 200))
+#' processed_data <- processMassList(data)
+#'
+processMassList <- function(data) {
+  data$NM <- round(data$Exp_mass)
+
+  # Process O
+  data$KM_O <- data$Exp_mass * (16 / 15.9949146223)
+  data$KMD_O <- round(data$NM - data$KM_O, 3)
+  data$z_O <- round(data$Exp_mass) %% 16 - 16
+
+  # Process H2
+  data$KM_H2 <- data$Exp_mass * (2 / 2.01565)
+  data$KMD_H2 <- round(data$NM - data$KM_H2, 3)
+  data$z_H2 <- round(data$Exp_mass) %% 2 - 2
+
+  return(data)
+}
+
+#' Process Known Compounds
+#'
+#' This function processes known compounds by merging them with a rest dataset and
+#' updating abundance and type columns based on specified columns and limits.
+#'
+#' @param rest The dataset containing the rest of the compounds.
+#' @param known The dataset containing known compounds.
+#' @param kmd_col The column name for KMD in both datasets.
+#' @param z_col The column name for Z in both datasets.
+#' @param num_col The column name to store the calculated number of compounds.
+#' @param type The column name to store the type of compound.
+#' @param step_limit The limit for considering compounds.
+#' @param remove_indices Indices of rows to be removed from the result.
+#'
+#' @return A processed dataset with updated abundance and type columns.
+#'
+#' @examples
+#' # Example Usage:
+#' processKnown(Rest, KnownO, "KMD_O", "z_O", "O_num", "O", 0.01, c(11, 13, 33))
+#'
+processKnown <- function(rest, known, kmd_col, z_col, num_col, type, step_limit, remove_indices) {
+  names(known)[2] <- "base_mass"
+
+  step_result <- merge(rest, known, by.x = c(kmd_col, z_col), by.y = c(kmd_col, z_col))
+  step_result[[num_col]] <- round((step_result$Exp_mass - step_result$base_mass) / step_limit)
+  step_result[[type]] <- step_result[[type]] + step_result[[num_col]]
+  step_result$Type <- type
+  step_result$form <- paste(step_result[c("C", "H", "O", "N", "S", "P", "E", "S34", "N15", "D", "Cl", "Fl", "Cl37", "M", "NH4", "POE", "NOE")], sep = "_")
+  step_result <- step_result[abs(step_result[[num_col]]) <= step_limit, ]
+  step_result <- step_result[-remove_indices, ]
+  return(step_result)
+}
+
 #' Generates a plot to visualize recalibrant series, recalibrates two mass lists,
 #' and produces a list of the chosen recalibrants.
 #'
@@ -96,36 +197,25 @@ Recal <- function(df,
   df$Adduct <- replace(df$Adduct, df$NOE == 1, "OE")
   df$series <- paste(df$class, df$Adduct, df$DBE, sep = "_")
   df$mode <- mode
-  names(df)[1] <- "Abundance"
-  names(df)[2] <- "Exp_mass"
+  names(df)[1:2] <- c("Abundance", "Exp_mass")
 
   if (cols == 2) {
     isopeaks <- ifelse(isopeaks == "none", data.frame(mass = 1, Abundance = 1, Tag = "X"), isopeaks) # Change 12/6/19
     # isopeaks <- if(isopeaks == "none") data.frame(mass = 1, Abundance = 1, Tag = "X") else isopeaks
     isopeaks <- data.frame(isopeaks)
     isopeaks <- isopeaks[, c(1:3)]
-    names(isopeaks)[1] <- "mass"
-    names(isopeaks)[2] <- "Abundance"
-    names(isopeaks)[3] <- "Tag"
-    names
-    names(peaks)[1] <- "mass"
-    names(peaks)[2] <- "Abundance"
-    peaks$Tag <- "X"
-    peaks$Tag2 <- "Mono"
+    isopeaks <- setNames(isopeaks, c("mass", "Abundance", "Tag"))
+    peaks <- setNames(peaks, c("Abundance", "mass"))
 
     isopeaks$Tag2 <- "Iso"
+    peaks$Tag <- "X"
+    peaks$Tag2 <- "Mono"
 
     # peaks <- if(isopeaks != "none") rbind(peaks, isopeaks) else peaks
     peaks <- ifelse(isopeaks != "none", rbind(peaks, isopeaks), peaks) # Change 12/6/19
     peaks <- data.frame(peaks)
     peaks <- peaks[, c(1:4)]
-    names(peaks)[1] <- "mass"
-    names(peaks)[2] <- "Abundance"
-    names(peaks)[3] <- "Tag"
-    names(peaks)[4] <- "Tag2"
-
-    peaks <- peaks[c(2, 1, 3, 4)]
-    # isodummy <- data.frame
+    peaks <- setNames(peaks[c(2, 1, 3, 4)], c("Abundance", "mass", "Tag", "Tag2"))
     isopeaks <- isopeaks[c(2, 1, 3, 4)]
 
     # Merges recalibrant list to df in order to determine which recalibrants are in the data frame.
@@ -140,58 +230,18 @@ Recal <- function(df,
       "Abundance", "Exp_mass", "C", "H", "O", "N", "S", "P", "E",
       "S34", "N15", "D", "Cl", "Fl", "Cl37", "M", "NH4", "POE", "NOE", "Z", "C13_mass"
     )]
-    RecalList$NM <- round(RecalList$Exp_mass)
-
-    RecalList$KM_O <- RecalList$Exp_mass * (16 / 15.9949146223)
-    RecalList$KMD_O <- round(RecalList$NM - RecalList$KM_O, 3)
-    RecalList$z_O <- round(RecalList$Exp_mass) %% 16 - 16
-
-    RecalList$KM_H2 <- RecalList$Exp_mass * (2 / 2.01565)
-    RecalList$KMD_H2 <- round(RecalList$NM - RecalList$KM_H2, 3)
-    RecalList$z_H2 <- round(RecalList$Exp_mass) %% 2 - 2
+    RecalList <- processMassList(RecalList)
 
     Rest <- df[c("Abundance", "Exp_mass")]
-    Rest$NM <- round(Rest$Exp_mass)
-
-    Rest$KM_O <- Rest$Exp_mass * (16 / 15.9949146223)
-    Rest$KMD_O <- round(Rest$NM - Rest$KM_O, 3)
-    Rest$z_O <- round(Rest$Exp_mass) %% 16 - 16
-
-    Rest$KM_H2 <- Rest$Exp_mass * (2 / 2.01565)
-    Rest$KMD_H2 <- round(Rest$NM - Rest$KM_H2, 3)
-    Rest$z_H2 <- round(Rest$Exp_mass) %% 2 - 2
+    Rest <- processMassList(Rest)
 
     ############
     # Picking recalibrants with series
-    knownO <- RecalList[c(1:21, 24, 25)]
-
-    names(knownO)[2] <- "base_mass"
-    Step2 <- merge(Rest, knownO, by.x = c("KMD_O", "z_O"), by.y = c("KMD_O", "z_O"))
-    Step2$O_num <- round(((Step2$Exp_mass - Step2$base_mass)) / 15.9949146223)
-    Step2$O <- Step2$O + Step2$O_num
-    Step2$Type <- "O"
-    Step2$form <- paste(Step2$C, Step2$H, Step2$O, Step2$N, Step2$S, Step2$P, Step2$E, Step2$S34,
-      Step2$N15, Step2$D, Step2$Cl, Step2$Fl, Step2$Cl37, Step2$M, Step2$NH4,
-      Step2$POE, Step2$NOE,
-      sep = "_"
-    )
-    Step2 <- Step2[abs(Step2$O_num) <= step_O, ]
+    # Process known_O
+    Step2 <- processKnown(Rest, RecalList[c(1:21, 24, 25)], "KMD_O", "z_O", "O_num", "O", step_O, c(10, 31))
 
-    Step2 <- Step2[-c(10, 31)]
-
-    knownH2 <- RecalList[c(1:21, 27, 28)]
-    names(knownH2)[2] <- "base_mass"
-    Step3 <- merge(Rest, knownH2, by.x = c("KMD_H2", "z_H2"), by.y = c("KMD_H2", "z_H2"))
-    Step3$H2_num <- round(((Step3$Exp_mass - Step3$base_mass)) / 2.01565)
-    Step3$H <- Step3$H + 2 * Step3$H2_num
-    Step3$Type <- "H2"
-    Step3$form <- paste(Step3$C, Step3$H, Step3$O, Step3$N, Step3$S, Step3$P, Step3$E, Step3$S34,
-      Step3$N15, Step3$D, Step3$Cl, Step3$Fl, Step3$Cl37, Step3$M, Step3$NH4,
-      Step3$POE, Step3$NOE,
-      sep = "_"
-    )
-    Step3 <- Step3[abs(Step3$H2_num) <= step_H2, ]
-    Step3 <- Step3[-c(10, 31)]
+    # Process known_H2
+    Step3 <- processKnown(Rest, RecalList[c(1:21, 27, 28)], "KMD_H2", "z_H2", "H2_num", "H2", step_H2, c(10, 31))
 
     Out <- rbind(Step2, Step3)
     Out2 <- dplyr::distinct(Out, Exp_mass)
@@ -206,7 +256,6 @@ Recal <- function(df,
     FinalRecal2 <- dplyr::group_by(NewRecal, Bin)
     # FinalRecal2 <- dplyr::top_n(FinalRecal, obs, Abundance)
 
-
     NewRecal_mono <- FinalRecal2[c(1:4)] # Changed 5/13/20
     NewRecal_iso <- FinalRecal2[c(5, 3, 4, 6)] # Changed 5/13/20
     NewRecal_iso <- NewRecal_iso[NewRecal_iso$C13_mass > 0, ]
@@ -222,28 +271,19 @@ Recal <- function(df,
 
     isopeaks <- data.frame(isopeaks)
     isopeaks <- isopeaks[, c(1:4)]
-    names(isopeaks)[1] <- "mass"
-    names(isopeaks)[2] <- "Abundance"
-    names(isopeaks)[3] <- "RT"
-    names(isopeaks)[4] <- "Tag"
-
-    names(peaks)[1] <- "mass"
-    names(peaks)[2] <- "Abundance"
-    names(peaks)[3] <- "RT"
-    peaks$Tag <- "X"
-    peaks$Tag2 <- "Mono"
+    isopeaks <- setNames(isopeaks, c("mass", "Abundance", "RT", "Tag"))
+    peaks <- setNames(peaks, c("mass", "Abundance", "RT"))
 
     isopeaks$Tag2 <- "Iso"
+    peaks$Tag <- "X"
+    peaks$Tag2 <- "Mono"
 
 
     peaks <- ifelse(isopeaks != "none", rbind(peaks, isopeaks), peaks) # Change 12/6/19
     peaks <- data.frame(peaks)
     peaks <- peaks[, c(1:5)]
-    names(peaks)[1] <- "mass"
-    names(peaks)[2] <- "Abundance"
-    names(peaks)[3] <- "RT"
-    names(peaks)[4] <- "Tag"
-    names(peaks)[5] <- "Tag2"
+    peaks <- setNames(peaks, c("mass", "Abundance", "RT", "Tag", "Tag2"))
+
 
     peaks <- peaks[c(2, 1, 3, 4, 5)]
     # isodummy <- data.frame
@@ -259,31 +299,16 @@ Recal <- function(df,
       "Abundance", "Exp_mass", "RT", "C", "H", "O", "N", "S", "P", "E",
       "S34", "N15", "D", "Cl", "Fl", "Cl37", "M", "NH4", "POE", "NOE", "Z", "C13_mass"
     )]
-    RecalList$NM <- round(RecalList$Exp_mass)
-
-    RecalList$KM_O <- RecalList$Exp_mass * (16 / 15.9949146223)
-    RecalList$KMD_O <- round(RecalList$NM - RecalList$KM_O, 3)
-    RecalList$z_O <- round(RecalList$Exp_mass) %% 16 - 16
-
-    RecalList$KM_H2 <- RecalList$Exp_mass * (2 / 2.01565)
-    RecalList$KMD_H2 <- round(RecalList$NM - RecalList$KM_H2, 3)
-    RecalList$z_H2 <- round(RecalList$Exp_mass) %% 2 - 2
+    RecalList <- processMassList(RecalList)
 
     Rest <- df[c("Abundance", "Exp_mass", "RT")]
-    Rest$NM <- round(Rest$Exp_mass)
-
-    Rest$KM_O <- Rest$Exp_mass * (16 / 15.9949146223)
-    Rest$KMD_O <- round(Rest$NM - Rest$KM_O, 3)
-    Rest$z_O <- round(Rest$Exp_mass) %% 16 - 16
-
-    Rest$KM_H2 <- Rest$Exp_mass * (2 / 2.01565)
-    Rest$KMD_H2 <- round(Rest$NM - Rest$KM_H2, 3)
-    Rest$z_H2 <- round(Rest$Exp_mass) %% 2 - 2
+    # Assuming RecalList is your data frame
+    Rest <- processMassList(Rest)
 
     ############
     # Picking recalibrants with series
-    knownO <- RecalList[c(1:22, 25, 26)] #
 
+    knownO <- RecalList[c(1:22, 25, 26)]
     names(knownO)[2] <- "base_mass"
     Step2 <- merge(Rest, knownO, by.x = c("KMD_O", "z_O"), by.y = c("KMD_O", "z_O"))
     Step2$O_num <- round(((Step2$Exp_mass - Step2$base_mass)) / 15.9949146223)
@@ -295,10 +320,9 @@ Recal <- function(df,
       sep = "_"
     )
     Step2 <- Step2[abs(Step2$O_num) <= step_O, ]
-
     Step2 <- Step2[-c(11, 13, 33)] #
 
-    knownH2 <- RecalList[c(1:22, 28, 29)] #
+    knownH2 <- RecalList[c(1:22, 28, 29)]
     names(knownH2)[2] <- "base_mass"
     Step3 <- merge(Rest, knownH2, by.x = c("KMD_H2", "z_H2"), by.y = c("KMD_H2", "z_H2"))
     Step3$H2_num <- round(((Step3$Exp_mass - Step3$base_mass)) / 2.01565)
@@ -439,28 +463,22 @@ Recal <- function(df,
   #########################
   # Calculating the weights needed for recalibration and calculating the recalibrated masses.
   if (cols == 2) {
-    names(NewRecal)[2] <- "E_mass"
-    names(NewRecal)[3] <- "formula"
-    names(NewRecal)[4] <- "Th_mass"
+    colnames(NewRecal)[c(2, 3, 4)] <- c("E_mass", "formula", "Th_mass")
     peaks_dum <- data.frame(Abundance = -2, mass = -2, Tag = "X", Tag2 = "Y", Range = -1)
-    peaks_out <- data.frame(Abundance = -2, mass = -2, Tag = "X", Tag2 = "Y", Range = -1)
+    peaks_out <- peaks_dum
     Ej_dum <- data.frame(Ejsum = -2, Range = -2)
-    Ej_out <- data.frame(Ejsum = -2, Range = -2)
-    Dummy3 <- data.frame(Value = 1:max(NewRecal$Range))
-    Dummy3 <- as.numeric(nrow(Dummy3))
+    Ej_out <- Ej_dum
+    Dummy3 <- max(NewRecal$Range)
   }
 
 
   if (cols == 3) {
-    names(NewRecal)[2] <- "E_mass"
-    names(NewRecal)[4] <- "formula"
-    names(NewRecal)[5] <- "Th_mass"
+    colnames(NewRecal)[c(2, 4, 5)] <- c("E_mass", "formula", "Th_mass")
     peaks_dum <- data.frame(Abundance = -2, mass = -2, RT = -42, Tag = "X", Tag2 = "Y", Range = -1)
-    peaks_out <- data.frame(Abundance = -2, mass = -2, RT = -42, Tag = "X", Tag2 = "Y", Range = -1)
+    peaks_out <- peaks_dum
     Ej_dum <- data.frame(Ejsum = -2, Range = -2)
-    Ej_out <- data.frame(Ejsum = -2, Range = -2)
-    Dummy3 <- data.frame(Value = 1:max(NewRecal$Range))
-    Dummy3 <- as.numeric(nrow(Dummy3))
+    Ej_out <- Ej_dum
+    Dummy3 <- as.numeric(nrow(data.frame(Value = 1:max(NewRecal$Range))))
   }
   ############################
   peakshigh <- peaks[peaks$Range > max(NewRecal$Range), ]
@@ -514,13 +532,11 @@ Recal <- function(df,
 
   peaks <- rbind(peaks, peakshigh)
   # Separating the isotope and monoisotope masses for output.
-  isopeaks2 <- peaks[peaks$Tag2 != "Mono", ]
-  isopeaks2 <- isopeaks2[c(1, 2, 3, 4)]
-  names(isopeaks2)[2] <- "Iso_mass"
-  names(isopeaks2)[1] <- "Iso_Abund"
-  names(isopeaks2)[3] <- "Iso_RT"
-  peaks <- peaks[peaks$Tag2 == "Mono", ]
-  peaks <- peaks[c(1, 2, 3)]
+  isopeaks2 <- subset(peaks, Tag2 != "Mono", select = c(1, 2, 3, 4))
+  names(isopeaks2) <- c("Iso_Abund", "Iso_mass", "Iso_RT", "Tag2")
+
+  peaks <- subset(peaks, Tag2 == "Mono", select = c(1, 2, 3))
+  names(peaks) <- c("Abundance", "mass", "RT")
 
   ################################################
   # Recalibration for error comparison, calculating the average error before and after recalibration.
@@ -564,76 +580,43 @@ Recal <- function(df,
   if (cols == 3) {
     AbundM <- df[c("Exp_mass", "Abundance", "RT")]
     AbundI <- df[c("C13_mass", "C13_abund", "C13_RT")]
-    names(AbundI)[1] <- "Exp_mass"
-    names(AbundI)[2] <- "Abundance"
-    names(AbundI)[3] <- "RT"
+    colnames(AbundI) <- c("Exp_mass", "Abundance", "RT")
     Abund <- rbind(AbundM, AbundI)
     Abund <- Abund[Abund$Abundance > 0, ]
 
     RecalPlot <- merge(RecalOut, Abund, by.x = c("Exp_mass", "Abundance", "RT"), by.y = c("Exp_mass", "Abundance", "RT"))
 
     RecalOut <- RecalOut[c(3, 5, 6, 9, 10)]
+
+    # Preparing the final output of the function, the recalibrants list.
+    peaks <- setNames(peaks[c(2, 1, 3)], c("exp_mass", "abundance", names(peaks)[3]))
+    isopeaks2 <- setNames(isopeaks2[c(2, 1, 3, 4)], c("iso_mass", "iso_abund", names(isopeaks2)[3], "tag"))
+    names(RecalOut)[c(1, 2)] <- c("abundance", "exp_mass")
   }
 
   if (cols == 2) {
     AbundM <- df[c("Exp_mass", "Abundance")]
     AbundI <- df[c("C13_mass", "C13_abund")]
-    names(AbundI)[1] <- "Exp_mass"
-    names(AbundI)[2] <- "Abundance"
+    names(AbundI) <- c("Exp_mass", "Abundance")
     Abund <- rbind(AbundM, AbundI)
     Abund <- Abund[Abund$Abundance > 0, ]
 
     RecalPlot <- merge(RecalOut, Abund, by.x = c("Exp_mass", "Abundance"), by.y = c("Exp_mass", "Abundance"))
 
     RecalOut <- RecalOut[c(2, 3, 4, 7, 8)]
-  }
 
-  # Plot highlighting the recalibrant ions for qualitative assessment.
-  MZ <- ggplot2::ggplot() +
-    ggplot2::geom_segment(data = plotpeak, size = 0.7, ggplot2::aes_string(x = "mass", xend = "mass", y = 0, yend = "Abundance"), alpha = 0.3, color = "grey57") +
-    ggplot2::geom_segment(data = RecalPlot, size = 1.2, ggplot2::aes_string(x = "Exp_mass", xend = "Exp_mass", y = 0, yend = "Abundance"), color = "blue") +
-    ggplot2::theme_bw() +
-    ggplot2::labs(x = "Ion Mass", y = "Abundance", title = "Assignment Mass Spectrum", color = "Series") +
-    ggplot2::theme(
-      axis.title = ggplot2::element_text(size = 15, face = "bold"), strip.text = ggplot2::element_text(size = 15, face = "bold"),
-      axis.text = ggplot2::element_text(size = 15, face = "bold"), legend.title = ggplot2::element_text(face = "bold", size = 15),
-      legend.text = ggplot2::element_text(face = "bold", size = 15), panel.grid.minor.x = ggplot2::element_blank(),
-      panel.grid.major.x = ggplot2::element_blank(), strip.background = ggplot2::element_blank(),
-      plot.title = ggplot2::element_text(size = 16, face = "bold")
-    )
-
-  # Preparing the final output of the function, the recalibrants list.
-
-
-  if (cols == 3) {
-    peaks <- peaks[c(2, 1, 3)]
-    names(peaks)[2] <- "abundance"
-    names(peaks)[1] <- "exp_mass"
-    isopeaks2 <- isopeaks2[c(2, 1, 3, 4)]
-    names(isopeaks2)[2] <- "iso_abund"
-    names(isopeaks2)[1] <- "iso_mass"
-    names(isopeaks2)[4] <- "tag"
-    names(RecalOut)[1] <- "abundance"
-    names(RecalOut)[2] <- "exp_mass"
+    # Preparing the final output of the function, the recalibrants list.
+    peaks <- setNames(peaks[c(2, 1)], c("exp_mass", "abundance"))
+    isopeaks2 <- setNames(isopeaks2[c(2, 1, 3)], c("iso_mass", "iso_abund", "tag"))
+    names(RecalOut)[c(1, 2)] <- c("abundance", "exp_mass")
   }
 
-  if (cols == 2) {
-    peaks <- peaks[c(2, 1)]
-    names(peaks)[2] <- "abundance"
-    names(peaks)[1] <- "exp_mass"
-    isopeaks2 <- isopeaks2[c(2, 1, 3)]
-    names(isopeaks2)[2] <- "iso_abund"
-    names(isopeaks2)[1] <- "iso_mass"
-    names(isopeaks2)[3] <- "tag"
-    names(RecalOut)[1] <- "abundance"
-    names(RecalOut)[2] <- "exp_mass"
-  }
+  # Plot highlighting the recalibrant ions for qualitative assessment.
+  MZ <- plot_spectrum(plotpeak, RecalPlot)
 
   Output <- list(Plot = MZ, Mono = peaks, Iso = isopeaks2, RecalList = RecalOut)
-  Output
 }
 
-
 #' Identifies canditate series for recalibration
 #'
 #' RecalList() takes the output from MFAssign() and/or MFAssignCHO()