diff --git a/NAMESPACE b/NAMESPACE
index 0caf33f..8516a73 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -40,6 +40,7 @@ export(testaaPop)
 export(times)
 export(uigetfile)
 export(uiputfile)
+export(writeMixtureInfo)
 importFrom(methods,is)
 importFrom(stats,runif)
 importFrom(utils,read.delim)
diff --git a/R/writeMixtureInfo.R b/R/writeMixtureInfo.R
new file mode 100644
index 0000000..174781c
--- /dev/null
+++ b/R/writeMixtureInfo.R
@@ -0,0 +1,337 @@
+#' @title Write Mixture Info
+#' @description Writes information about the mixture
+#' @param logml logml
+#' @param rowsFromInd rowsFromInd
+#' @param data data
+#' @param adjprior adjprior
+#' @param priorTerm priorTerm
+#' @param outPutFile outPutFile
+#' @param inputFile inputFile
+#' @param partitionSummary partitionSummary
+#' @param popnames popnames
+#' @param fixedK fixedK
+#' @param PARTITION PARTITION
+#' @param COUNTS COUNTS
+#' @param SUMCOUNTS SUMCOUNTS
+#' @param LOGDIFF LOGDIFF
+#' @return changesInLogml
+#' @export
+writeMixtureInfo <- function(
+	logml, rowsFromInd, data, adjprior, priorTerm, outPutFile, inputFile, partitionSummary, popnames, fixedK, PARTITION, COUNTS, SUMCOUNTS,
+	LOGDIFF
+) {
+
+	changesInLogml <- list()
+	ninds <- size(data, 1) / rowsFromInd
+	npops <- size(COUNTS, 3)
+	# Check that the names refer to individuals
+	names <- (size(popnames, 1) == ninds) #Tarkistetaan ett?nimet viittaavat yksil�ihin
+
+	if (length(outPutFile) > 0) {
+		fid <- load(outPutFile)
+	} else {
+		fid <- -1
+		message('Diverting output to baps4_output.baps')
+		# TODO: replace sink with option that will record input and output
+		sink('baps4_output.baps', split=TRUE) # save in text anyway.
+	}
+
+	dispLine()
+	cat('RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:')
+	cat(c('Data file: ', inputFile))
+	cat('Model: independent')
+	cat(c('Number of clustered individuals: ', ownNum2Str(ninds)))
+	cat(c('Number of groups in optimal partition: ', ownNum2Str(npops)))
+	cat(c('Log(marginal likelihood) of optimal partition: ', ownNum2Str(logml)))
+	cat(' ')
+	if (fid != -1) {
+		append(fid, 'RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:\n')
+		append(fid, c('Data file: ', inputFile, '\n'))
+		append(
+			fid,
+			c('Number of clustered individuals: ', ownNum2Str(ninds), '\n')
+		)
+		append(
+			fid,
+			c(
+				'Number of groups in optimal partition: ',
+				ownNum2Str(npops), '\n'
+			)
+		)
+		append(
+			fid,
+			c(
+				'Log(marginal likelihood) of optimal partition: ',
+				ownNum2Str(logml),
+				'\n'
+			)
+		)
+	}
+
+	cluster_count <- length(unique(PARTITION))
+	cat('Best Partition: ')
+	if (fid != -1) {
+		append(fid, c('Best Partition: ', '\n'))
+	}
+	for (m in 1:cluster_count) {
+		indsInM <- find(PARTITION == m)
+		length_of_beginning <- 11 + floor(log10(m))
+		cluster_size <- length(indsInM)
+
+		if (names) {
+			text <- c(
+				'Cluster ',
+				as.character(m),
+				': {',
+				as.character(popnames[[indsInM[1]]])
+			)
+			for (k in 2:cluster_size) {
+				text <- c(text, ', ', as.character(popnames[[indsInM[k]]]))
+			}
+		} else {
+			text <- c(
+				'Cluster ', as.character(m), ': {', as.character(indsInM[1])
+			)
+			for (k in 2:cluster_size) {
+				text <- c(text, ', ', as.character(indsInM[k]))
+			}
+		}
+		text <- c(text, '}')
+		while (length(text) > 58) {
+			# Take one line and display it.
+			new_line <- takeLine(text, 58)
+			text <- (length(new_line) + 1):end
+			cat(new_line)
+			if (fid != -1) {
+				append(fid, new_line)
+				append(fid,'\n')
+			}
+			if (length(text) > 0) {
+				text <- c(blanks(length_of_beginning), text)
+			} else {
+				text <- ""
+			}
+		}
+		if (text != "") {
+			cat(text)
+			if (fid != -1) {
+				append(fid, text)
+				append(fid,'\n')
+			}
+		}
+	}
+
+	if (npops > 1) {
+		cat(' ')
+		cat(' ')
+		cat(
+			'Changes in log(marginal likelihood)',
+			' if indvidual i is moved to group j:'
+		)
+		if (fid != -1) {
+			append(fid, ' ')
+			append(fid, '\n')
+			append(fid, ' ')
+			append(fid, '\n')
+			append(
+				fid,
+				c(
+					'Changes in log(marginal likelihood)',
+					'if indvidual i is moved to group j:'
+				)
+			)
+			append(fid, '\n')
+		}
+
+		if (names) {
+			nameSizes <- zeros(ninds, 1)
+			for (i in 1:ninds) {
+				nimi <- as.character(popnames[i])
+				nameSizes[i] <- length(nimi)
+			}
+			maxSize <- max(nameSizes)
+			maxSize <- max(maxSize, 5)
+			erotus <- maxSize - 5
+			alku <- blanks(erotus)
+			ekarivi <- c(alku, '  ind', blanks(6 + erotus))
+		} else {
+			ekarivi <- '  ind      '
+		}
+		for (i in 1:cluster_count) {
+			ekarivi <- c(ekarivi, ownNum2Str(i), blanks(8 - floor(log10(i))))
+		}
+		cat(ekarivi)
+		if (fid != -1) {
+			append(fid, ekarivi)
+			append(fid, '\n')
+		}
+
+		# %ninds = size(data,1)/rowsFromInd;
+		changesInLogml <- t(LOGDIFF)
+		for (ind in 1:ninds) {
+			muutokset <- changesInLogml[, ind]
+
+			if (names) {
+				nimi <- as.character(popnames[ind])
+				rivi <- c(blanks(maxSize - length(nimi)), nimi, ':')
+			} else {
+				rivi <- c(blanks(4 - floor(log10(ind))), ownNum2Str(ind), ':')
+			}
+			for (j in 1:npops) {
+				rivi <- c(rivi, '  ', logml2String(omaRound(muutokset[j])))
+			}
+			cat(rivi)
+			if (fid != -1) {
+				append(fid, rivi)
+				append(fid, '\n')
+			}
+		}
+
+		cat(' ')
+		cat(' ')
+		cat('KL-divergence matrix in PHYLIP format:')
+
+		dist_mat <- zeros(npops, npops)
+		if (fid != -1) {
+			append(fid, ' ')
+			append(fid, ' ')
+			append(fid, c('KL-divergence matrix in PHYLIP format:'))
+			append(fid, '\n')
+		}
+
+		maxnoalle <- size(COUNTS, 1)
+		nloci <- size(COUNTS, 2)
+		d <- zeros(maxnoalle, nloci, npops)
+		prior <- adjprior
+		prior[find(prior == 1)] <- 0
+		nollia <- find(all(prior == 0)) # Loci in which only one allele was detected.
+		prior[1, nollia] <- 1
+		for (pop1 in 1:npops) {
+			d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) /
+				repmat(sum(squeeze(COUNTS[, , pop1]) + prior), c(maxnoalle, 1))
+		}
+		ekarivi <- as.character(npops)
+		cat(ekarivi)
+		if (fid != -1) {
+			append(fid, ekarivi)
+			append(fid, '\n')
+		}
+
+		for (pop1 in 1:npops) {
+			for (pop2 in 1:(pop1 - 1)) {
+				dist1 <- d[, , pop1]
+				dist2 <- d[, , pop2]
+				div12 <- sum(
+					sum(dist1 * log2((dist1 + 10 ^ -10) / (dist2 + 10 ^ -10)))
+				) / nloci
+				div21 <- sum(
+					sum(dist2 * log2((dist2 + 10 ^ -10) / (dist1 + 10 ^ -10)))
+				) / nloci
+				div <- (div12 + div21) / 2
+				dist_mat(pop1, pop2) <- div
+			}
+		}
+
+
+		dist_mat <- dist_mat + t(dist_mat) # make it symmetric
+		for (pop1 in 1:npops) {
+			rivi <- c('Cluster_', as.character(pop1), ' ')
+			for (pop2 in 1:npops) {
+				rivi <- c(rivi, kldiv2str(dist_mat[pop1, pop2]), ' ')
+			}
+			cat(rivi)
+			if (fid != -1) {
+				append(fid, rivi)
+				append(fid, '\n')
+			}
+		}
+	}
+
+	cat(' ')
+	cat(' ');
+	cat(
+		'List of sizes of 10 best visited partitions',
+		'and corresponding log(ml) values'
+	)
+
+	if (fid != -1) {
+		append(fid, ' ')
+		append(fid, '\n')
+		append(fid, ' ')
+		append(fid, '\n')
+		append(
+			fid,
+			c(
+				'List of sizes of 10 best visited partitions',
+				'and corresponding log(ml) values'
+			)
+		)
+		append(fid, '\n')
+	}
+
+	partitionSummary <- sortrows(partitionSummary, 2)
+	partitionSummary <- partitionSummary[size(partitionSummary, 1):1, ]
+	partitionSummary <- partitionSummary[find(partitionSummary[, 2] > -1e49), ]
+	if (size(partitionSummary, 1) > 10) {
+		vikaPartitio <- 10
+	} else {
+		vikaPartitio <- size(partitionSummary, 1)
+	}
+	for (part in 1:vikaPartitio) {
+		line <- c(
+			as.character(partitionSummary[part, 1]),
+			'    ',
+			as.character(partitionSummary(part, 2))
+		)
+		cat(line)
+		if (fid != -1) {
+			append(fid, line)
+			append(fid, '\n')
+		}
+	}
+
+	if (!fixedK) {
+		cat(' ')
+		cat(' ')
+		cat('Probabilities for number of clusters')
+
+		if (fid != -1) {
+			append(fid, ' ')
+			append(fid, '\n')
+			append(fid, ' ')
+			append(fid, '\n')
+			append(fid, c('Probabilities for number of clusters'))
+			append(fid, '\n')
+		}
+
+		npopsTaulu <- unique(partitionSummary[, 1])
+		len <- length(npopsTaulu)
+		probs <- zeros(len, 1)
+		partitionSummary[, 2] <- partitionSummary[, 2] -
+			max(partitionSummary[, 2])
+		sumtn <- sum(exp(partitionSummary[, 2]))
+		for (i in 1:len) {
+			npopstn <- sum(
+				exp(
+					partitionSummary[find(
+						partitionSummary[, 1] == npopsTaulu[i]
+					), 2]
+				)
+			)
+			probs[i] <- npopstn / sumtn
+		}
+		for (i in 1:len) {
+			if (probs[i] > 1e-5) {
+				line <- c(
+					as.character(npopsTaulu[i]), '   ', as.character(probs[i])
+				)
+				cat(line)
+				if (fid != -1) {
+					append(fid, line)
+					append(fid, '\n')
+				}
+			}
+		}
+	}
+	return(changesInLogml)
+}
\ No newline at end of file
diff --git a/man/writeMixtureInfo.Rd b/man/writeMixtureInfo.Rd
new file mode 100644
index 0000000..f9a1682
--- /dev/null
+++ b/man/writeMixtureInfo.Rd
@@ -0,0 +1,58 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/writeMixtureInfo.R
+\name{writeMixtureInfo}
+\alias{writeMixtureInfo}
+\title{Write Mixture Info}
+\usage{
+writeMixtureInfo(
+  logml,
+  rowsFromInd,
+  data,
+  adjprior,
+  priorTerm,
+  outPutFile,
+  inputFile,
+  partitionSummary,
+  popnames,
+  fixedK,
+  PARTITION,
+  COUNTS,
+  SUMCOUNTS,
+  LOGDIFF
+)
+}
+\arguments{
+\item{logml}{logml}
+
+\item{rowsFromInd}{rowsFromInd}
+
+\item{data}{data}
+
+\item{adjprior}{adjprior}
+
+\item{priorTerm}{priorTerm}
+
+\item{outPutFile}{outPutFile}
+
+\item{inputFile}{inputFile}
+
+\item{partitionSummary}{partitionSummary}
+
+\item{popnames}{popnames}
+
+\item{fixedK}{fixedK}
+
+\item{PARTITION}{PARTITION}
+
+\item{COUNTS}{COUNTS}
+
+\item{SUMCOUNTS}{SUMCOUNTS}
+
+\item{LOGDIFF}{LOGDIFF}
+}
+\value{
+changesInLogml
+}
+\description{
+Writes information about the mixture
+}