diff --git a/DESCRIPTION b/DESCRIPTION
index 1f077d4..767c151 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: stackr
 Type: Package
 Title: GBS/RAD Data Exploration, Manipulation and Visualization using R
-Version: 0.1.2
-Date: 2015-07-16
+Version: 0.1.3
+Date: 2015-09-03
 Encoding: UTF-8
 Authors@R: c(
   person("Thierry", "Gosselin", email = "thierrygosselin@icloud.com", role = c("aut", "cre")),
diff --git a/NAMESPACE b/NAMESPACE
index 31810fa..8080bdb 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -48,6 +48,7 @@ export(summary_stats_pop)
 export(summary_stats_vcf_tidy)
 export(sumstats_prep)
 export(table_low_coverage_summary)
+export(vcf_imputation)
 export(whitelist_loci)
 export(whitelist_loci_snp)
 export(whitelist_loci_vcf)
diff --git a/R/haplo2colony.R b/R/haplo2colony.R
index a3698d5..23cb8d3 100644
--- a/R/haplo2colony.R
+++ b/R/haplo2colony.R
@@ -743,7 +743,7 @@ haplo2colony <- function(haplotypes.file,
         # Automatically select all the core -1 
         options(rf.cores=detectCores()-1, mc.cores=detectCores()-1)
       } else {
-        options(rf.cores = parallel.core, mc.cores = mc.cores)
+        options(rf.cores = parallel.core, mc.cores = parallel.core)
       }
       
       # imputations using Random Forest with the package randomForestSRC
diff --git a/R/haplo2genepop.R b/R/haplo2genepop.R
index bc970bc..3eeefca 100644
--- a/R/haplo2genepop.R
+++ b/R/haplo2genepop.R
@@ -371,7 +371,7 @@ haplo2genepop <- function(haplotypes.file,
         # Automatically select all the core -1 
         options(rf.cores=detectCores()-1, mc.cores=detectCores()-1)
       } else {
-        options(rf.cores = parallel.core, mc.cores = mc.cores)
+        options(rf.cores = parallel.core, mc.cores = parallel.core)
       }
       
       # imputations using Random Forest with the package randomForestSRC
diff --git a/R/haplo2genind.R b/R/haplo2genind.R
index 646085c..5f40fe9 100644
--- a/R/haplo2genind.R
+++ b/R/haplo2genind.R
@@ -315,7 +315,7 @@ haplo2genind <- function(haplotypes.file,
         # Automatically select all the core -1 
         options(rf.cores=detectCores()-1, mc.cores=detectCores()-1)
       } else {
-        options(rf.cores = parallel.core, mc.cores = mc.cores)
+        options(rf.cores = parallel.core, mc.cores = parallel.core)
       }
       
       # imputations using Random Forest with the package randomForestSRC
diff --git a/R/vcf_imputation.R b/R/vcf_imputation.R
new file mode 100644
index 0000000..818f042
--- /dev/null
+++ b/R/vcf_imputation.R
@@ -0,0 +1,406 @@
+# VCF data imputation using Random Forest
+
+
+#' @name vcf_imputation
+#' @title VCF data imputation
+#' @description Map-independent imputation of a VCF using Random Forest or the 
+#' most frequent category.
+#' @param vcf.file The VCF file created by STACKS.
+#' @param pop.id.start The start of your population id 
+#' in the name of your individual sample.
+#' @param pop.id.end The end of your population id 
+#' in the name of your individual sample.
+#' @param whitelist.loci (optional) A whitelist of loci and 
+#' a column header 'LOCUS'.
+#' The whitelist is in the directory (e.g. "whitelist.txt").
+#' @param blacklist.id (optional) A blacklist with individual ID and
+#' a column header 'INDIVIDUALS'. The blacklist is in the directory
+#'  (e.g. "blacklist.txt").
+#' @param blacklist.genotypes A blacklist of genotypes 
+#' containing 3 columns header 'LOCUS', 'POS' and 'INDIVIDUALS'.
+#' @param filename The name of the file written to the directory.
+#' Use the extension ".vcf" at the end. Default \code{batch_1.vcf.imputed.vcf}.
+#' @param imputations For map-independent imputations available options are:
+#' (1) \code{"max"} to use the most frequent category for imputations.
+#'  (2) \code{"rf"} using Random Forest algorithm. Default = \code{"rf"}.
+#' @param imputations.group \code{"global"} or \code{"populations"}.
+#' Should the imputations be computed globally or by populations. If you choose
+#' global, turn the verbose to \code{TRUE}, to see progress.
+#' Default = \code{"populations"}.
+#' @param num.tree The number of trees to grow in Random Forest. Default is 100.
+#' @param iteration.rf The number of iterations of missing data algorithm 
+#' in Random Forest. Default is 10.
+#' @param split.number Non-negative integer value used to specify 
+#' random splitting in Random Forest. Default is 100.
+#' @param verbose Logical. Should trace output be enabled on each iteration 
+#' in Random Forest ? Default is \code{FALSE}.
+#' @param parallel.core (optional) The number of core for OpenMP 
+#' shared-memory parallel programming of Random Forest imputations. 
+#' For more info on how to install the
+#' OpenMP version see \code{\link[randomForestSRC]{randomForestSRC-package}}.
+#' If not selected \code{detectCores()-1} is used as default.
+#' @details The imputations using Random Forest requires more time to compute 
+#' and can take several minutes and hours depending on the size of 
+#' the dataset and polymorphism of the species used. e.g. with a 
+#' low polymorphic taxa, and a data set containing 30\% missing data, 
+#' 5 000 haplotypes loci and 500 individuals will require 15 min.
+#' @return When no imputation is selected ....
+#' @export
+#' @rdname vcf_imputation
+#' @import reshape2
+#' @import dplyr
+#' @import tidyr
+#' @import lazyeval
+#' @importFrom stringr str_pad
+#' @references Catchen JM, Amores A, Hohenlohe PA et al. (2011) 
+#' Stacks: Building and Genotyping Loci De Novo From Short-Read Sequences. 
+#' G3, 1, 171-182.
+#' @references Catchen JM, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) 
+#' Stacks: an analysis tool set for population genomics. 
+#' Molecular Ecology, 22, 3124-3140.
+#' @references Ishwaran H. and Kogalur U.B. (2015). Random Forests for Survival,
+#'  Regression and Classification (RF-SRC), R package version 1.6.1.
+#' @references Ishwaran H. and Kogalur U.B. (2007). Random survival forests
+#' for R. R News 7(2), 25-31.
+#' @references Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008).
+#' Random survival forests. Ann. Appl. Statist. 2(3), 841--860.
+#' @seealso \code{randomForestSRC} is available on 
+#' CRAN \url{http://cran.r-project.org/web/packages/randomForestSRC/} 
+#' and github \url{https://github.com/ehrlinger/randomForestSRC}
+#' @author Thierry Gosselin \email{thierrygosselin@@icloud.com}
+
+vcf_imputation <- function(vcf.file, 
+                           pop.id.start, 
+                           pop.id.end, 
+                           blacklist.id = NULL,
+                           whitelist.loci = NULL,
+                           blacklist.genotypes = NULL,
+                           filename = "batch_1.vcf.imputed.vcf",
+                           imputations = "rf",
+                           imputations.group = "populations",
+                           num.tree = 100,
+                           iteration.rf = 10,
+                           split.number = 100,
+                           verbose = FALSE,
+                           parallel.core = 2
+) {
+  # remove NOTE about no visible binding for global variable during Build 
+  QUAL <- NULL
+  FILTER <- NULL
+  FORMAT <- NULL
+  FORMAT_ID <- NULL
+  ID <- NULL
+  '#CHROM' <- NULL
+  INFO <- NULL
+  REF <- NULL
+  ALT <- NULL
+  READ_DEPTH <- NULL
+  ALLELE_DEPTH <- NULL
+  GT <- NULL
+  GL <- NULL
+  MARKER <- NULL
+  everything <- NULL
+  
+  # Import/read VCF ------------------------------------------------------------- 
+  message("Importing the VCF...")
+  
+  vcf <- read_delim(
+    vcf.file, delim = "\t", 
+    skip = 9,#n_max = max.read.lines,
+    progress = interactive()
+  ) %>%
+    rename(CHROM = `#CHROM`)
+  
+  # Whitelist-------------------------------------------------------------------
+  if (missing(whitelist.loci) == "FALSE" & is.vector(whitelist.loci) == "TRUE") {
+    message("Using the whitelist from the directory")
+    whitelist <- read_tsv(whitelist.loci, col_names = T) %>%
+      rename(ID = LOCUS)
+  } else if (missing(whitelist.loci) == "FALSE" & is.vector(whitelist.loci) == "FALSE") {
+    message("Using whitelist from your global environment")
+    whitelist <- whitelist.loci %>%
+      rename(ID = LOCUS)
+  } else {
+    message("No whitelist")
+    whitelist <- NULL
+  }
+  
+  # Blacklist-------------------------------------------------------------------
+  if (missing(blacklist.id) == "FALSE" & is.vector(blacklist.id) == "TRUE") {
+    message("Using the blacklisted id from the directory")
+    blacklist.id <- read_tsv(blacklist.id, col_names = T)    
+  } else if (missing(blacklist.id) == "FALSE" & is.vector(blacklist.id) == "FALSE") {
+    message("Using the blacklisted id from your global environment")
+    blacklist.id <- blacklist.id
+    
+  } else {
+    message("No individual blacklisted")
+    blacklist.id <- NULL
+  }
+  
+  
+  if (is.null(whitelist.loci) == TRUE & is.null(blacklist.id) == TRUE) {
+    
+    # Combination 1: No whitelist and No blacklist -----------------------------
+    vcf <- vcf    
+    
+  } else if (is.null(whitelist.loci) == FALSE & is.null(blacklist.id) == TRUE) {
+    
+    # Combination 2: Using whitelist, but No blacklist -------------------------
+    
+    # just whitelist.loci, NO Blacklist of individual
+    vcf <- vcf %>% group_by(ID) %>% 
+      semi_join(whitelist, by = "ID") %>% 
+      arrange(ID)
+    
+  } else if (is.null(whitelist.loci) == TRUE & is.null(blacklist.id) == FALSE) {
+    
+    # Combination 3: Using a blacklist of id, but No whitelist -----------------
+    
+    # NO whitelist, JUST Blacklist of individual
+    vcf <- vcf %>% group_by(ID) %>%
+      mutate(INDIVIDUALS = as.character(INDIVIDUALS)) %>%
+      anti_join(blacklist.id, by = "INDIVIDUALS") %>%
+      arrange(ID)
+    
+  } else {
+    # Combination 4: Using a whitelist and blacklist---------------------------
+    
+    # whitelist.loci + Blacklist of individual
+    vcf <- vcf %>% 
+      group_by(ID) %>%
+      semi_join(whitelist, by = "ID") %>%
+      mutate(INDIVIDUALS = as.character(INDIVIDUALS)) %>%
+      anti_join(blacklist.id, by = "INDIVIDUALS") %>%
+      arrange(ID)
+  }
+  
+  # dump unused object
+  whitelist <- NULL
+  blacklist.id <- NULL
+  
+  
+  # VCF prep before imputation -------------------------------------------------
+  vcf.keeper <- vcf %>%
+    select(CHROM, ID, POS, REF, ALT) %>% 
+    unite(MARKER, CHROM, ID, POS, sep = "_", remove = TRUE)
+  
+  vcf <- vcf %>% 
+    select(-c(REF, ALT, QUAL, FILTER, INFO, FORMAT)) %>% 
+    gather(INDIVIDUALS, FORMAT_ID, -c(CHROM, ID, POS)) %>%
+    separate(FORMAT_ID, c("GT", "READ_DEPTH", "ALLELE_DEPTH", "GL"),
+             sep = ":", extra = "error") %>% 
+    select(-c(READ_DEPTH, ALLELE_DEPTH, GL))
+  
+  
+  # Erasing genotypes with poor coverage and/or genotype likelihood-------------
+  if (is.null(blacklist.genotypes) == FALSE) {
+    message("Erasing genotypes in the VCF using the list of blacklisted genotypes...")
+    
+    bad.geno <- read_tsv(blacklist.genotypes, col_names = T) %>%
+      mutate(INDIVIDUALS = as.character(INDIVIDUALS)) %>%
+      rename(ID = LOCUS) %>% 
+      select(-POP_ID)
+    
+    # interesting stats.
+    erased.genotype.number <- length(bad.geno$INDIVIDUALS)
+    total.genotype.number <- length(vcf$GT[vcf$GT != "./."])
+    genotype.percent <- paste(round(((erased.genotype.number/total.genotype.number)*100), 4), "%", sep = " ")
+    
+    vcf <- vcf %>%
+      mutate(
+        GT = ifelse(ID %in% bad.geno$ID & POS %in% bad.geno$POS & INDIVIDUALS %in% bad.geno$INDIVIDUALS, "./.", GT)
+      )
+    
+    # Get the number of imputed genotypes and percentage
+    imputed.genotype.number <- length(vcf$GT[vcf$GT == "./."])
+    imputed.genotype.percent <- paste(round((imputed.genotype.number/(imputed.genotype.number+total.genotype.number))*100, 4), "%", sep = " ")
+
+  } else {
+    vcf <- vcf
+    
+    # interesting stats
+    erased.genotype.number <- 0
+    total.genotype.number <- length(vcf$GT[vcf$GT != "./."])
+    genotype.percent <- paste(round(((erased.genotype.number/total.genotype.number)*100), 4), "%", sep = " ")
+    imputed.genotype.number <- length(vcf$GT[vcf$GT == "./."])
+    imputed.genotype.percent <- paste(round((imputed.genotype.number/(imputed.genotype.number+total.genotype.number))*100, 4), "%", sep = " ")
+    
+  }
+  # dump unused objects
+  bad.geno <- NULL
+  
+  # last step before imputation ------------------------------------------------
+  
+  vcf <- vcf %>%
+    unite(MARKER, CHROM, ID, POS, sep = "_", remove = TRUE) %>% 
+    mutate(GT = stri_replace_all_fixed(GT, "./.", "NA", vectorize_all=F)) %>% 
+    mutate(POP_ID = substr(INDIVIDUALS, pop.id.start, pop.id.end)) %>%
+    dcast(INDIVIDUALS + POP_ID ~ MARKER, value.var = "GT")
+  
+  if (imputations == "max"){
+    message("Calculating map-independent imputations using the most frequent allele.")
+  } else {
+    message("Calculating map-independent imputations using random forest")
+  }
+  
+  # Imputations: VCF with imputed haplotypes using Random Forest ------------
+  if (imputations == "rf") {
+    # Parallel computations options
+    if (missing(parallel.core) == "TRUE"){
+      # Automatically select all the core -1 
+      options(rf.cores=detectCores()-1, mc.cores=detectCores()-1)
+    } else {
+      options(rf.cores = parallel.core, mc.cores = parallel.core)
+    }
+    
+    # imputations using Random Forest with the package randomForestSRC
+    
+    impute_markers_rf <- function(x){
+      randomForestSRC::impute.rfsrc(data = x, 
+                                    ntree = num.tree, 
+                                    nodesize = 1, 
+                                    nsplit = split.number, 
+                                    nimpute = iteration.rf, 
+                                    do.trace = verbose)
+    }
+    
+    # imputations by populations (default) or globally -------------------------
+    
+    # default by pop
+    if (missing(imputations.group) == "TRUE" | imputations.group == "populations"){
+      message("Imputations computed by populations, take a break...")
+      
+      df.split.pop <- split(x = vcf, f = vcf$POP_ID) # slip data frame by population
+      pop.list <- names(df.split.pop) # list the pop
+      imputed.dataset <-list() # create empty list 
+      for (i in pop.list) {
+        sep.pop <- df.split.pop[[i]]
+        sep.pop <- suppressWarnings(
+          plyr::colwise(factor, exclude = "NA")(sep.pop)
+        )
+        imputed.dataset[[i]] <- impute_markers_rf(sep.pop)
+        # message of progress for imputations by population
+        pop.imputed <- paste("Completed imputations for pop ", i, sep = "")
+        message(pop.imputed)
+      }
+      vcf.imp <- suppressWarnings(as.data.frame(bind_rows(imputed.dataset)))
+      
+      # dump unused objects
+      df.split.pop <- NULL
+      pop.list <- NULL
+      sep.pop <- NULL
+      imputed.dataset <- NULL
+      
+    } else if (imputations.group == "global"){
+      # Globally (not by pop_id)
+      message("Imputations computed globally, take a break...")
+      vcf <- plyr::colwise(factor, exclude = "NA")(vcf)
+      vcf.imp <- impute_markers_rf(vcf)
+    } 
+    
+  } else if (imputations == "max") {
+    
+    if (missing(imputations.group) == "TRUE" | imputations.group == "populations"){
+      message("Imputations computed by populations")
+      
+      vcf.imp <- vcf %>%
+        gather(MARKER, GT, -c(INDIVIDUALS, POP_ID)) %>%
+        mutate(GT = replace(GT, which(GT=="NA"), NA)) %>%
+        group_by(MARKER, POP_ID) %>% 
+        mutate(GT = stri_replace_na(GT, replacement = max(GT, na.rm = TRUE)))
+      
+    } else if (imputations.group == "global"){
+      # Globally (not by pop_id)
+      message("Imputations computed globally")
+      
+      vcf.imp <- vcf %>%
+        gather(MARKER, GT, -c(INDIVIDUALS, POP_ID)) %>%
+        mutate(GT = replace(GT, which(GT=="NA"), NA)) %>%
+        group_by(MARKER) %>% 
+        mutate(GT = stri_replace_na(GT, replacement = max(GT, na.rm = TRUE)))
+    }
+  }
+  
+  # transform the imputed dataset back into a VCF format------------------------
+  
+  vcf.imp <- suppressWarnings(
+    vcf.imp %>% 
+      gather(MARKER, GT, -c(INDIVIDUALS, POP_ID)) %>%
+      arrange(POP_ID, INDIVIDUALS) %>% 
+      group_by(MARKER) %>% 
+      mutate(INFO = stri_paste("NS=", n(), sep = "")) %>% 
+      select(-POP_ID) %>% 
+      dcast(MARKER + INFO ~ INDIVIDUALS, value.var = "GT")
+  )
+  
+  vcf.imp <- suppressWarnings(
+    full_join(vcf.keeper, vcf.imp, by = "MARKER") %>%
+      separate(MARKER, c("CHROM", "ID", "POS"), sep = "_", extra = "error") %>%
+      mutate(
+        ID = as.numeric(ID),
+        POS = as.numeric(POS),
+        QUAL = rep(".", n()),
+        FILTER = rep("PASS", n()),
+        FORMAT = rep("GT", n())
+      ) %>% 
+      arrange(CHROM, ID, POS) %>% 
+      select('#CHROM' = CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO, FORMAT, everything())
+  )
+  
+  # Write VCF header to the file ---------------------------------------------
+  
+  # filename
+  if (missing(filename) == "TRUE"){
+    filename <- "batch_1.vcf.imputed.vcf"
+  } else {
+    filename <- filename
+  }
+  
+  # File format
+  file.format <- "##fileformat=VCFv4.0"
+  file.format <- as.data.frame(file.format)
+  write.table(x = file.format, file = filename, sep = " ", append = FALSE, col.names = FALSE, row.names = FALSE, quote = FALSE)
+  
+  # File date
+  file.date <- stri_replace_all_fixed(Sys.Date(), pattern = "-", replacement = "")
+  file.date <- stri_paste("##fileDate=", file.date, sep = "")
+  write.table(x = file.date, file = filename, sep = " ", append = TRUE, col.names = FALSE, row.names = FALSE, quote = FALSE)
+  
+  # Source
+  file.source <- as.data.frame(stri_paste("##source=stackr v.", packageVersion("stackr"), sep = ""))
+  write.table(x = file.source, file = filename, sep = " ", append = TRUE, col.names = FALSE, row.names = FALSE, quote = FALSE)
+  
+  # Info field 1
+  info1 <- '##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data">'
+  info1 <- as.data.frame(info1)
+  write.table(x = info1, file = filename, sep = " ", append = TRUE, col.names = FALSE, row.names = FALSE, quote = FALSE)
+  
+  #     # Info field 2
+  #     info2 <- '##INFO=<ID=AF,Number=.,Type=Float,Description="Allele Frequency">'
+  #     info2 <- as.data.frame(info2)
+  #     write.table(x = info2, file = filename, sep = " ", append = FALSE, col.names = FALSE, row.names = FALSE, quote = FALSE)
+  
+  # Format field 1
+  format1 <- '##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">'
+  format1 <- as.data.frame(format1)
+  write.table(x = format1, file = filename, sep = " ", append = TRUE, col.names = FALSE, row.names = FALSE, quote = FALSE)
+  
+  # Write the imputed vcf to the file
+  suppressWarnings(
+    write.table(x = vcf.imp, file = filename, sep = "\t", append = TRUE, col.names = TRUE, row.names = FALSE, quote = FALSE)
+  )
+  
+  invisible(cat(sprintf(
+    "VCF imputation
+Total of %s genotypes 
+Erased genotypes: %s (= %s )
+Imputation of %s genotypes (%s)\n
+Filename:
+%s
+Written in the directory:
+%s",
+    total.genotype.number, erased.genotype.number, genotype.percent, imputed.genotype.number, imputed.genotype.percent, filename, getwd()
+  )))
+  
+}
diff --git a/man/vcf_imputation.Rd b/man/vcf_imputation.Rd
new file mode 100644
index 0000000..15dc5b8
--- /dev/null
+++ b/man/vcf_imputation.Rd
@@ -0,0 +1,102 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/vcf_imputation.R
+\name{vcf_imputation}
+\alias{vcf_imputation}
+\title{VCF data imputation}
+\usage{
+vcf_imputation(vcf.file, pop.id.start, pop.id.end, blacklist.id = NULL,
+  whitelist.loci = NULL, blacklist.genotypes = NULL,
+  filename = "batch_1.vcf.imputed.vcf", imputations = "rf",
+  imputations.group = "populations", num.tree = 100, iteration.rf = 10,
+  split.number = 100, verbose = FALSE, parallel.core = 2)
+}
+\arguments{
+\item{vcf.file}{The VCF file created by STACKS.}
+
+\item{pop.id.start}{The start of your population id
+in the name of your individual sample.}
+
+\item{pop.id.end}{The end of your population id
+in the name of your individual sample.}
+
+\item{blacklist.id}{(optional) A blacklist with individual ID and
+a column header 'INDIVIDUALS'. The blacklist is in the directory
+ (e.g. "blacklist.txt").}
+
+\item{whitelist.loci}{(optional) A whitelist of loci and
+a column header 'LOCUS'.
+The whitelist is in the directory (e.g. "whitelist.txt").}
+
+\item{blacklist.genotypes}{A blacklist of genotypes
+containing 3 columns header 'LOCUS', 'POS' and 'INDIVIDUALS'.}
+
+\item{filename}{The name of the file written to the directory.
+Use the extension ".vcf" at the end. Default \code{batch_1.vcf.imputed.vcf}.}
+
+\item{imputations}{For map-independent imputations available options are:
+(1) \code{"max"} to use the most frequent category for imputations.
+ (2) \code{"rf"} using Random Forest algorithm. Default = \code{"rf"}.}
+
+\item{imputations.group}{\code{"global"} or \code{"populations"}.
+Should the imputations be computed globally or by populations. If you choose
+global, turn the verbose to \code{TRUE}, to see progress.
+Default = \code{"populations"}.}
+
+\item{num.tree}{The number of trees to grow in Random Forest. Default is 100.}
+
+\item{iteration.rf}{The number of iterations of missing data algorithm
+in Random Forest. Default is 10.}
+
+\item{split.number}{Non-negative integer value used to specify
+random splitting in Random Forest. Default is 100.}
+
+\item{verbose}{Logical. Should trace output be enabled on each iteration
+in Random Forest ? Default is \code{FALSE}.}
+
+\item{parallel.core}{(optional) The number of core for OpenMP
+shared-memory parallel programming of Random Forest imputations.
+For more info on how to install the
+OpenMP version see \code{\link[randomForestSRC]{randomForestSRC-package}}.
+If not selected \code{detectCores()-1} is used as default.}
+}
+\value{
+When no imputation is selected ....
+}
+\description{
+Map-independent imputation of a VCF using Random Forest or the
+most frequent category.
+}
+\details{
+The imputations using Random Forest requires more time to compute
+and can take several minutes and hours depending on the size of
+the dataset and polymorphism of the species used. e.g. with a
+low polymorphic taxa, and a data set containing 30\% missing data,
+5 000 haplotypes loci and 500 individuals will require 15 min.
+}
+\author{
+Thierry Gosselin \email{thierrygosselin@icloud.com}
+}
+\references{
+Catchen JM, Amores A, Hohenlohe PA et al. (2011)
+Stacks: Building and Genotyping Loci De Novo From Short-Read Sequences.
+G3, 1, 171-182.
+
+Catchen JM, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013)
+Stacks: an analysis tool set for population genomics.
+Molecular Ecology, 22, 3124-3140.
+
+Ishwaran H. and Kogalur U.B. (2015). Random Forests for Survival,
+ Regression and Classification (RF-SRC), R package version 1.6.1.
+
+Ishwaran H. and Kogalur U.B. (2007). Random survival forests
+for R. R News 7(2), 25-31.
+
+Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008).
+Random survival forests. Ann. Appl. Statist. 2(3), 841--860.
+}
+\seealso{
+\code{randomForestSRC} is available on
+CRAN \url{http://cran.r-project.org/web/packages/randomForestSRC/}
+and github \url{https://github.com/ehrlinger/randomForestSRC}
+}
+