diff --git a/DESCRIPTION b/DESCRIPTION
index 941b3d3..58d8f6e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: PureCN
 Type: Package
 Title: Copy number calling and SNV classification using
     targeted short read sequencing
-Version: 1.23.12
+Version: 1.23.13
 Date: 2021-08-12
 Authors@R: c(person("Markus", "Riester",
                     role = c("aut", "cre"),
diff --git a/NEWS b/NEWS
index 00fd636..0183dd2 100755
--- a/NEWS
+++ b/NEWS
@@ -10,6 +10,9 @@ SIGNIFICANT USER-VISIBLE CHANGES
       sometimes exceeded the noise.
     o Added min.variants argument to runAbsoluteCN
     o Added PureCN version to runAbsoluteCN results object (ret$version)
+    o Do not attempt two-step segmentation in PSCBS when off-target noise is still
+      very small (< 0.15). Happens with super clean data and can lead to over-segmentation
+      due to other biases.
 
 BUGFIXES
 
diff --git a/R/runAbsoluteCN.R b/R/runAbsoluteCN.R
index 08f1d91..0b11a19 100644
--- a/R/runAbsoluteCN.R
+++ b/R/runAbsoluteCN.R
@@ -572,7 +572,7 @@ runAbsoluteCN <- function(normal.coverage.file = NULL,
         seg = segProvided, plot.cnv = plot.cnv,
         sampleid = sampleid, vcf = vcf.germline, tumor.id.in.vcf = tumor.id.in.vcf,
         normal.id.in.vcf = normal.id.in.vcf, max.segments = max.segments, chr.hash = chr.hash,
-        centromeres = centromeres), args.segmentation)
+        min.logr.sdev = min.logr.sdev, centromeres = centromeres), args.segmentation)
 
     vcf.germline <- NULL
     seg <- do.call(fun.segmentation,
diff --git a/R/segmentationCBS.R b/R/segmentationCBS.R
index 34282c0..2aa9613 100644
--- a/R/segmentationCBS.R
+++ b/R/segmentationCBS.R
@@ -27,6 +27,9 @@
 #' @param normal.id.in.vcf Id of normal in in VCF. Currently not used.
 #' @param max.segments If not \code{NULL}, try a higher \code{undo.SD}
 #' parameter if number of segments exceeds the threshold.
+#' @param min.logr.sdev Minimum log-ratio standard deviation used in the
+#' model. Useful to make fitting more robust to outliers in very clean
+#' data (currently not used in this segmentation function).
 #' @param prune.hclust.h Height in the \code{hclust} pruning step. Increasing
 #' this value will merge segments more aggressively. If NULL, try to find a
 #' sensible default.
@@ -76,7 +79,8 @@
 segmentationCBS <- function(normal, tumor, log.ratio, seg, plot.cnv,
     sampleid, weight.flag.pvalue = 0.01, alpha = 0.005,
     undo.SD = NULL, vcf = NULL, tumor.id.in.vcf = 1, normal.id.in.vcf = NULL,
-    max.segments = NULL, prune.hclust.h = NULL, prune.hclust.method = "ward.D",
+    max.segments = NULL, min.logr.sdev = 0.15,
+    prune.hclust.h = NULL, prune.hclust.method = "ward.D",
     chr.hash = NULL, additional.cmd.args = "", centromeres = NULL) {
 
     if (is.null(chr.hash)) chr.hash <- .getChrHash(seqlevels(tumor))
diff --git a/R/segmentationGATK4.R b/R/segmentationGATK4.R
index 4228497..bc23004 100644
--- a/R/segmentationGATK4.R
+++ b/R/segmentationGATK4.R
@@ -18,6 +18,9 @@
 #' @param tumor.id.in.vcf Id of tumor in case multiple samples are stored in
 #' VCF.
 #' @param normal.id.in.vcf Id of normal in in VCF. Currently not used.
+#' @param min.logr.sdev Minimum log-ratio standard deviation used in the
+#' model. Useful to make fitting more robust to outliers in very clean
+#' data (currently not used in this segmentation function).
 #' @param prune.hclust.h Ignored in this function.
 #' @param prune.hclust.method Ignored in this function.
 #' @param changepoints.penality The \code{--number-of-changepoints-penalty-factor}.
@@ -59,7 +62,7 @@
 #' @export segmentationGATK4
 segmentationGATK4 <- function(normal, tumor, log.ratio, seg, 
     vcf = NULL, tumor.id.in.vcf = 1, normal.id.in.vcf = NULL,
-    prune.hclust.h = NULL, prune.hclust.method = NULL,
+    min.logr.sdev = 0.15, prune.hclust.h = NULL, prune.hclust.method = NULL,
     changepoints.penality = NULL, additional.cmd.args = "",
     chr.hash = NULL, ...) {
 
diff --git a/R/segmentationHclust.R b/R/segmentationHclust.R
index 6c5e868..7ad3194 100644
--- a/R/segmentationHclust.R
+++ b/R/segmentationHclust.R
@@ -17,6 +17,9 @@
 #' @param tumor.id.in.vcf Id of tumor in case multiple samples are stored in
 #' VCF.
 #' @param normal.id.in.vcf Id of normal in in VCF. Currently not used.
+#' @param min.logr.sdev Minimum log-ratio standard deviation used in the
+#' model. Useful to make fitting more robust to outliers in very clean
+#' data (currently not used in this segmentation function).
 #' @param prune.hclust.h Height in the \code{hclust} pruning step. Increasing
 #' this value will merge segments more aggressively. If NULL, try to find a
 #' sensible default.
@@ -50,7 +53,7 @@
 #' @export segmentationHclust
 segmentationHclust <- function(seg,
     vcf = NULL, tumor.id.in.vcf = 1, normal.id.in.vcf = NULL,
-    prune.hclust.h = NULL, prune.hclust.method = "ward.D",
+    min.logr.sdev = 0.15, prune.hclust.h = NULL, prune.hclust.method = "ward.D",
     chr.hash = NULL, ...) {
     if (is.null(seg)) {
         .stopUserError("segmentationHclust requires an input segmentation.")  
diff --git a/R/segmentationPSCBS.R b/R/segmentationPSCBS.R
index b084583..1b811d3 100644
--- a/R/segmentationPSCBS.R
+++ b/R/segmentationPSCBS.R
@@ -1,11 +1,11 @@
 #' PSCBS segmentation
-#' 
+#'
 #' Alternative segmentation function using the \code{PSCBS} package.  This
 #' function is called via the \code{fun.segmentation} argument of
 #' \code{\link{runAbsoluteCN}}.  The arguments are passed via
 #' \code{args.segmentation}.
-#' 
-#' 
+#'
+#'
 #' @param normal Coverage data for normal sample. Ignored in this function.
 #' @param tumor Coverage data for tumor sample.
 #' @param log.ratio Copy number log-ratios, one for each exon in coverage file.
@@ -26,64 +26,67 @@
 #' @param vcf Optional VCF object with germline allelic ratios.
 #' @param tumor.id.in.vcf Id of tumor in case multiple samples are stored in
 #' VCF.
-#' @param normal.id.in.vcf Id of normal in in VCF. If \code{NULL}, 
+#' @param normal.id.in.vcf Id of normal in in VCF. If \code{NULL},
 #' use unpaired PSCBS.
-#' @param max.segments If not \code{NULL}, try a higher \code{undo.SD} 
+#' @param max.segments If not \code{NULL}, try a higher \code{undo.SD}
 #' parameter if number of segments exceeds the threshold.
 #' @param boost.on.target.max.size When off-target regions are noisy
 #' compared to on-target, try to find small segments of specified
 #' maximum size that might be missed to due the increased noise.
 #' Set to 0 to turn boosting off.
+#' @param min.logr.sdev Minimum log-ratio standard deviation used in the
+#' model. Useful to make fitting more robust to outliers in very clean
+#' data.
 #' @param prune.hclust.h Height in the \code{hclust} pruning step. Increasing
-#' this value will merge segments more aggressively. If \code{NULL}, try to 
+#' this value will merge segments more aggressively. If \code{NULL}, try to
 #' find a sensible default.
 #' @param prune.hclust.method Cluster method used in the \code{hclust} pruning
 #' step. See documentation for the \code{hclust} function.
 #' @param chr.hash Mapping of non-numerical chromsome names to numerical names
-#' (e.g. chr1 to 1, chr2 to 2, etc.). If \code{NULL}, assume chromsomes are 
+#' (e.g. chr1 to 1, chr2 to 2, etc.). If \code{NULL}, assume chromsomes are
 #' properly ordered.
 #' @param additional.cmd.args \code{character(1)}. Ignored.
 #' @param centromeres A \code{GRanges} with centromere positions.
-#' If not \code{NULL}, add breakpoints at centromeres. 
-#' @param \dots Additional parameters passed to the 
+#' If not \code{NULL}, add breakpoints at centromeres.
+#' @param \dots Additional parameters passed to the
 #' \code{segmentByNonPairedPSCBS} function.
 #' @return \code{data.frame} containing the segmentation.
 #' @author Markus Riester
-#' @references Olshen, A. B., Venkatraman, E. S., Lucito, R., Wigler, M. 
-#' (2004). Circular binary segmentation for the analysis of array-based DNA 
+#' @references Olshen, A. B., Venkatraman, E. S., Lucito, R., Wigler, M.
+#' (2004). Circular binary segmentation for the analysis of array-based DNA
 #' copy number data. Biostatistics 5: 557-572.
 #'
-#' Venkatraman, E. S., Olshen, A. B. (2007). A faster circular binary 
-#' segmentation algorithm for the analysis of array CGH data. Bioinformatics 
+#' Venkatraman, E. S., Olshen, A. B. (2007). A faster circular binary
+#' segmentation algorithm for the analysis of array CGH data. Bioinformatics
 #' 23: 657-63.
 #'
-#' Olshen et al. (2011). Parent-specific copy number in paired tumor-normal 
+#' Olshen et al. (2011). Parent-specific copy number in paired tumor-normal
 #' studies using circular binary segmentation. Bioinformatics.
 #' @seealso \code{\link{runAbsoluteCN}}
 #' @examples
-#' 
-#' normal.coverage.file <- system.file("extdata", "example_normal_tiny.txt", 
-#'     package="PureCN")
-#' tumor.coverage.file <- system.file("extdata", "example_tumor_tiny.txt", 
-#'     package="PureCN")
+#'
+#' normal.coverage.file <- system.file("extdata", "example_normal_tiny.txt",
+#'     package = "PureCN")
+#' tumor.coverage.file <- system.file("extdata", "example_tumor_tiny.txt",
+#'     package = "PureCN")
 #' vcf.file <- system.file("extdata", "example.vcf.gz",
-#'     package="PureCN")
-#' 
+#'     package = "PureCN")
+#'
 #' # The max.candidate.solutions, max.ploidy and test.purity parameters are set to
 #' # non-default values to speed-up this example.  This is not a good idea for real
 #' # samples.
-#'  ret <-runAbsoluteCN(normal.coverage.file=normal.coverage.file, 
-#'      tumor.coverage.file=tumor.coverage.file, vcf.file=vcf.file, 
-#'      sampleid="Sample1",  genome="hg19",
-#'      fun.segmentation=segmentationPSCBS, max.ploidy=4,
-#'      test.purity=seq(0.3,0.7,by=0.05), max.candidate.solutions=1)
-#' 
+#'  ret <-runAbsoluteCN(normal.coverage.file = normal.coverage.file,
+#'      tumor.coverage.file = tumor.coverage.file, vcf.file = vcf.file,
+#'      sampleid = "Sample1",  genome = "hg19",
+#'      fun.segmentation = segmentationPSCBS, max.ploidy = 4,
+#'      test.purity = seq(0.3, 0.7, by = 0.05), max.candidate.solutions = 1)
+#'
 #' @export segmentationPSCBS
-segmentationPSCBS <- function(normal, tumor, log.ratio, seg, plot.cnv, 
-    sampleid, weight.flag.pvalue = 0.01, alpha = 0.005, 
+segmentationPSCBS <- function(normal, tumor, log.ratio, seg, plot.cnv,
+    sampleid, weight.flag.pvalue = 0.01, alpha = 0.005,
     undo.SD = NULL, flavor = "tcn&dh", tauA = 0.03, vcf = NULL,
     tumor.id.in.vcf = 1, normal.id.in.vcf = NULL, max.segments = NULL,
-    boost.on.target.max.size = 30,
+    boost.on.target.max.size = 30, min.logr.sdev = 0.15,
     prune.hclust.h = NULL, prune.hclust.method = "ward.D", chr.hash = NULL,
     additional.cmd.args = "", centromeres = NULL, ...) {
 
@@ -92,13 +95,13 @@ segmentationPSCBS <- function(normal, tumor, log.ratio, seg, plot.cnv,
     }
 
     if (is.null(chr.hash)) chr.hash <- .getChrHash(seqlevels(tumor))
-    
+
     use.weights <- FALSE
-    if (!is.null(tumor$weights) && length(unique(tumor$weights)) > 1 ) {
+    if (!is.null(tumor$weights) && length(unique(tumor$weights)) > 1) {
         flog.info("Interval weights found, will use weighted PSCBS.")
         use.weights <- TRUE
     }
-    input <- .PSCBSinput(tumor, log.ratio, vcf, tumor.id.in.vcf, 
+    input <- .PSCBSinput(tumor, log.ratio, vcf, tumor.id.in.vcf,
                          normal.id.in.vcf, chr.hash)
 
     knownSegmentsCentromeres <- .PSCBSgetKnownSegments(centromeres, chr.hash)
@@ -111,39 +114,41 @@ segmentationPSCBS <- function(normal, tumor, log.ratio, seg, plot.cnv,
         knownSegments <- knownSegmentsCentromeres
         if (any(!input$on.target) &&
             boost.on.target.max.size > 0 &&
-            .robustSd(input$CT[input$on.target]) * 1.5 < 
-            .robustSd(input$CT[!input$on.target])) {
+            .robustSd(input$CT[input$on.target]) * 1.5 <
+            .robustSd(input$CT[!input$on.target]) &&
+            .robustSd(input$CT[!input$on.target]) > min.logr.sdev
+            ) {
             flog.info("On-target much cleaner than off-target, finding on-target breakpoints first...")
             idxot <- input$on.target
             if (use.weights) {
                 idxot <- input$on.target & input$weights >= median(input$weights, na.rm = TRUE)
                 if (!is.null(input$mappability) && any(!is.na(input$mappability))) {
                     idxot <- idxot & !is.na(input$mappability) & input$mappability > 0.9
-                }    
+                }
             }
             flog.info("Using %i high quality (out of %i) on-target intervals for initial breakpoint calculation.",
-                sum(idxot), sum(input$on.target)) 
-            segPSCBSot <- PSCBS::segmentByNonPairedPSCBS(input[idxot,], tauA=tauA, 
-                flavor=flavor, undoTCN=undo.SD, knownSegments=knownSegments, 
-                min.width=3,alphaTCN=alpha/2, ...)
+                sum(idxot), sum(input$on.target))
+            segPSCBSot <- PSCBS::segmentByNonPairedPSCBS(input[idxot, ], tauA = tauA,
+                flavor = flavor, undoTCN = undo.SD, knownSegments = knownSegments, 
+                min.width = 3,alphaTCN = alpha / 2, ...)
             segot <- .PSCBSoutput2DNAcopy(segPSCBSot, sampleid)
             if (!is.null(vcf)) {
-                segot <- .pruneByHclust(segot, vcf, tumor.id.in.vcf, h=prune.hclust.h, 
-                    method=prune.hclust.method, chr.hash=chr.hash)
+                segot <- .pruneByHclust(segot, vcf, tumor.id.in.vcf, h = prune.hclust.h,
+                    method = prune.hclust.method, chr.hash = chr.hash)
             }
-            segot <- segot[segot$num.mark > 3 & 
+            segot <- segot[segot$num.mark > 3 &
                            segot$num.mark <= boost.on.target.max.size, 2:4]
             colnames(segot) <- colnames(knownSegments)[1:3]
             knownSegments <- .PSCBSgetKnownSegments(centromeres, chr.hash, segot)
-        }    
-        segPSCBS <- PSCBS::segmentByNonPairedPSCBS(input, tauA=tauA, 
-            flavor=flavor, undoTCN=undo.SD, knownSegments=knownSegments, 
-            min.width=3,alphaTCN=alpha, ...)
+        }
+        segPSCBS <- PSCBS::segmentByNonPairedPSCBS(input, tauA = tauA,
+            flavor = flavor, undoTCN = undo.SD, knownSegments = knownSegments,
+            min.width = 3,alphaTCN = alpha, ...)
         try(flog.debug("Kappa: %f", PSCBS::estimateKappa(segPSCBS)), silent = TRUE)
         seg <- .PSCBSoutput2DNAcopy(segPSCBS, sampleid)
         if (undo.SD <= 0 || is.null(max.segments) || nrow(seg) < max.segments) break
         flog.info("Found %i segments, exceeding max.segments threshold of %i.",
-            nrow(seg), max.segments)    
+            nrow(seg), max.segments)
         undo.SD <- undo.SD * 1.5
         try.again <- try.again + 1
     }
@@ -152,40 +157,40 @@ segmentationPSCBS <- function(normal, tumor, log.ratio, seg, plot.cnv,
     segPSCBS <- NULL
 
     if (!is.null(vcf)) {
-        seg <- .pruneByHclust(seg, vcf, tumor.id.in.vcf, h=prune.hclust.h, 
-            method=prune.hclust.method, chr.hash=chr.hash)
+        seg <- .pruneByHclust(seg, vcf, tumor.id.in.vcf, h = prune.hclust.h,
+            method = prune.hclust.method, chr.hash = chr.hash)
     }
     seg <- .addAverageWeights(seg, weight.flag.pvalue, tumor, chr.hash)
     seg <- .fixBreakpointsInBaits(tumor, log.ratio, seg, chr.hash)
     seg
 }
 
-.PSCBSinput <- function(tumor, log.ratio, vcf, tumor.id.in.vcf, 
+.PSCBSinput <- function(tumor, log.ratio, vcf, tumor.id.in.vcf,
                         normal.id.in.vcf, chr.hash) {
     if (is.null(tumor$weights)) tumor$weights <- 1
     if (is.null(vcf)) {
         .stopUserError("segmentationPSCBS requires VCF file.")
-    }         
+    }
     ov <- findOverlaps(vcf, tumor)
-    d.f <- cbind(as.data.frame(tumor[subjectHits(ov)]), 
-        CT = 2 ^ (log.ratio+1)[subjectHits(ov)], 
-        betaT = unlist(geno(vcf[queryHits(ov)])$FA[,tumor.id.in.vcf]), 
+    d.f <- cbind(as.data.frame(tumor[subjectHits(ov)]),
+        CT = 2 ^ (log.ratio+1)[subjectHits(ov)],
+        betaT = unlist(geno(vcf[queryHits(ov)])$FA[, tumor.id.in.vcf]),
         betaN = NA,
         x = start(vcf[queryHits(ov)]),
         w = tumor$weights[subjectHits(ov)])
-    
+
     if (!is.null(normal.id.in.vcf)) {
-        d.f$betaN <- unlist(geno(vcf[queryHits(ov)])$FA[,normal.id.in.vcf])
+        d.f$betaN <- unlist(geno(vcf[queryHits(ov)])$FA[, normal.id.in.vcf])
     }
 
-    d.f.2 <- cbind(as.data.frame(tumor[-subjectHits(ov)]), 
-        CT = 2 ^ (log.ratio+1)[-subjectHits(ov)], betaT=NA, betaN=NA,
+    d.f.2 <- cbind(as.data.frame(tumor[-subjectHits(ov)]),
+        CT = 2 ^ (log.ratio+1)[-subjectHits(ov)], betaT = NA, betaN = NA,
         x = start(tumor[-subjectHits(ov)]),
         w = tumor$weights[-subjectHits(ov)])
     
     d.f <- rbind(d.f, d.f.2)
     colnames(d.f)[1] <- "chromosome"
-    d.f <- d.f[order(.strip.chr.name(d.f[,1], chr.hash), d.f$x),]
+    d.f <- d.f[order(.strip.chr.name(d.f[, 1], chr.hash), d.f$x), ]
     d.f$chromosome <- .strip.chr.name(d.f$chromosome, chr.hash)
 
     if (min(tumor$weights) == max(tumor$weights)) {
@@ -199,21 +204,21 @@ segmentationPSCBS <- function(normal, tumor, log.ratio, seg, plot.cnv,
     if (is.null(centromeres)) return(NULL)
     knownSegments <- data.frame(centromeres)
     colnames(knownSegments)[1] <- "chromosome"
-    knownSegments$length <- knownSegments$end-knownSegments$start+1
+    knownSegments$length <- knownSegments$end - knownSegments$start + 1
     knownSegments$chromosome <- .strip.chr.name(knownSegments$chromosome,
         chr.hash)
     if (!is.null(breakpoints)) {
         knownSegments <- rbind(knownSegments[, 1:3], breakpoints)
-    }    
+    }
     PSCBS::gapsToSegments(knownSegments)
 }
 
 .PSCBSoutput2DNAcopy <- function(seg, sampleid) {
-    sx <- cbind(ID=sampleid, seg$output[!is.na(seg$output$tcnMean),])
-    sx <- sx[,c("ID", "chromosome", "tcnStart", "tcnEnd", "tcnNbrOfLoci", 
+    sx <- cbind(ID = sampleid, seg$output[!is.na(seg$output$tcnMean), ])
+    sx <- sx[, c("ID", "chromosome", "tcnStart", "tcnEnd", "tcnNbrOfLoci",
         "tcnMean")]
-    colnames(sx) <- c("ID", "chrom", "loc.start",  "loc.end", "num.mark", 
+    colnames(sx) <- c("ID", "chrom", "loc.start",  "loc.end", "num.mark",
         "seg.mean")
-    sx$seg.mean <- log2(sx$seg.mean/2)
+    sx$seg.mean <- log2(sx$seg.mean / 2)
     sx
 }
diff --git a/man/segmentationCBS.Rd b/man/segmentationCBS.Rd
index 44b83bf..3217989 100644
--- a/man/segmentationCBS.Rd
+++ b/man/segmentationCBS.Rd
@@ -18,6 +18,7 @@ segmentationCBS(
   tumor.id.in.vcf = 1,
   normal.id.in.vcf = NULL,
   max.segments = NULL,
+  min.logr.sdev = 0.15,
   prune.hclust.h = NULL,
   prune.hclust.method = "ward.D",
   chr.hash = NULL,
@@ -61,6 +62,10 @@ VCF.}
 \item{max.segments}{If not \code{NULL}, try a higher \code{undo.SD}
 parameter if number of segments exceeds the threshold.}
 
+\item{min.logr.sdev}{Minimum log-ratio standard deviation used in the
+model. Useful to make fitting more robust to outliers in very clean
+data (currently not used in this segmentation function).}
+
 \item{prune.hclust.h}{Height in the \code{hclust} pruning step. Increasing
 this value will merge segments more aggressively. If NULL, try to find a
 sensible default.}
diff --git a/man/segmentationGATK4.Rd b/man/segmentationGATK4.Rd
index c2f0ba0..8b879d2 100644
--- a/man/segmentationGATK4.Rd
+++ b/man/segmentationGATK4.Rd
@@ -12,6 +12,7 @@ segmentationGATK4(
   vcf = NULL,
   tumor.id.in.vcf = 1,
   normal.id.in.vcf = NULL,
+  min.logr.sdev = 0.15,
   prune.hclust.h = NULL,
   prune.hclust.method = NULL,
   changepoints.penality = NULL,
@@ -39,6 +40,10 @@ VCF.}
 
 \item{normal.id.in.vcf}{Id of normal in in VCF. Currently not used.}
 
+\item{min.logr.sdev}{Minimum log-ratio standard deviation used in the
+model. Useful to make fitting more robust to outliers in very clean
+data (currently not used in this segmentation function).}
+
 \item{prune.hclust.h}{Ignored in this function.}
 
 \item{prune.hclust.method}{Ignored in this function.}
diff --git a/man/segmentationHclust.Rd b/man/segmentationHclust.Rd
index 3af6858..1758459 100644
--- a/man/segmentationHclust.Rd
+++ b/man/segmentationHclust.Rd
@@ -9,6 +9,7 @@ segmentationHclust(
   vcf = NULL,
   tumor.id.in.vcf = 1,
   normal.id.in.vcf = NULL,
+  min.logr.sdev = 0.15,
   prune.hclust.h = NULL,
   prune.hclust.method = "ward.D",
   chr.hash = NULL,
@@ -28,6 +29,10 @@ VCF.}
 
 \item{normal.id.in.vcf}{Id of normal in in VCF. Currently not used.}
 
+\item{min.logr.sdev}{Minimum log-ratio standard deviation used in the
+model. Useful to make fitting more robust to outliers in very clean
+data (currently not used in this segmentation function).}
+
 \item{prune.hclust.h}{Height in the \code{hclust} pruning step. Increasing
 this value will merge segments more aggressively. If NULL, try to find a
 sensible default.}
diff --git a/man/segmentationPSCBS.Rd b/man/segmentationPSCBS.Rd
index 34761a7..400bbfc 100644
--- a/man/segmentationPSCBS.Rd
+++ b/man/segmentationPSCBS.Rd
@@ -21,6 +21,7 @@ segmentationPSCBS(
   normal.id.in.vcf = NULL,
   max.segments = NULL,
   boost.on.target.max.size = 30,
+  min.logr.sdev = 0.15,
   prune.hclust.h = NULL,
   prune.hclust.method = "ward.D",
   chr.hash = NULL,
@@ -63,10 +64,10 @@ function.}
 \item{tumor.id.in.vcf}{Id of tumor in case multiple samples are stored in
 VCF.}
 
-\item{normal.id.in.vcf}{Id of normal in in VCF. If \code{NULL}, 
+\item{normal.id.in.vcf}{Id of normal in in VCF. If \code{NULL},
 use unpaired PSCBS.}
 
-\item{max.segments}{If not \code{NULL}, try a higher \code{undo.SD} 
+\item{max.segments}{If not \code{NULL}, try a higher \code{undo.SD}
 parameter if number of segments exceeds the threshold.}
 
 \item{boost.on.target.max.size}{When off-target regions are noisy
@@ -74,15 +75,19 @@ compared to on-target, try to find small segments of specified
 maximum size that might be missed to due the increased noise.
 Set to 0 to turn boosting off.}
 
+\item{min.logr.sdev}{Minimum log-ratio standard deviation used in the
+model. Useful to make fitting more robust to outliers in very clean
+data.}
+
 \item{prune.hclust.h}{Height in the \code{hclust} pruning step. Increasing
-this value will merge segments more aggressively. If \code{NULL}, try to 
+this value will merge segments more aggressively. If \code{NULL}, try to
 find a sensible default.}
 
 \item{prune.hclust.method}{Cluster method used in the \code{hclust} pruning
 step. See documentation for the \code{hclust} function.}
 
 \item{chr.hash}{Mapping of non-numerical chromsome names to numerical names
-(e.g. chr1 to 1, chr2 to 2, etc.). If \code{NULL}, assume chromsomes are 
+(e.g. chr1 to 1, chr2 to 2, etc.). If \code{NULL}, assume chromsomes are
 properly ordered.}
 
 \item{additional.cmd.args}{\code{character(1)}. Ignored.}
@@ -90,7 +95,7 @@ properly ordered.}
 \item{centromeres}{A \code{GRanges} with centromere positions.
 If not \code{NULL}, add breakpoints at centromeres.}
 
-\item{\dots}{Additional parameters passed to the 
+\item{\dots}{Additional parameters passed to the
 \code{segmentByNonPairedPSCBS} function.}
 }
 \value{
@@ -104,33 +109,33 @@ function is called via the \code{fun.segmentation} argument of
 }
 \examples{
 
-normal.coverage.file <- system.file("extdata", "example_normal_tiny.txt", 
-    package="PureCN")
-tumor.coverage.file <- system.file("extdata", "example_tumor_tiny.txt", 
-    package="PureCN")
+normal.coverage.file <- system.file("extdata", "example_normal_tiny.txt",
+    package = "PureCN")
+tumor.coverage.file <- system.file("extdata", "example_tumor_tiny.txt",
+    package = "PureCN")
 vcf.file <- system.file("extdata", "example.vcf.gz",
-    package="PureCN")
+    package = "PureCN")
 
 # The max.candidate.solutions, max.ploidy and test.purity parameters are set to
 # non-default values to speed-up this example.  This is not a good idea for real
 # samples.
- ret <-runAbsoluteCN(normal.coverage.file=normal.coverage.file, 
-     tumor.coverage.file=tumor.coverage.file, vcf.file=vcf.file, 
-     sampleid="Sample1",  genome="hg19",
-     fun.segmentation=segmentationPSCBS, max.ploidy=4,
-     test.purity=seq(0.3,0.7,by=0.05), max.candidate.solutions=1)
+ ret <-runAbsoluteCN(normal.coverage.file = normal.coverage.file,
+     tumor.coverage.file = tumor.coverage.file, vcf.file = vcf.file,
+     sampleid = "Sample1",  genome = "hg19",
+     fun.segmentation = segmentationPSCBS, max.ploidy = 4,
+     test.purity = seq(0.3, 0.7, by = 0.05), max.candidate.solutions = 1)
 
 }
 \references{
-Olshen, A. B., Venkatraman, E. S., Lucito, R., Wigler, M. 
-(2004). Circular binary segmentation for the analysis of array-based DNA 
+Olshen, A. B., Venkatraman, E. S., Lucito, R., Wigler, M.
+(2004). Circular binary segmentation for the analysis of array-based DNA
 copy number data. Biostatistics 5: 557-572.
 
-Venkatraman, E. S., Olshen, A. B. (2007). A faster circular binary 
-segmentation algorithm for the analysis of array CGH data. Bioinformatics 
+Venkatraman, E. S., Olshen, A. B. (2007). A faster circular binary
+segmentation algorithm for the analysis of array CGH data. Bioinformatics
 23: 657-63.
 
-Olshen et al. (2011). Parent-specific copy number in paired tumor-normal 
+Olshen et al. (2011). Parent-specific copy number in paired tumor-normal
 studies using circular binary segmentation. Bioinformatics.
 }
 \seealso{