diff --git a/.travis.yml b/.travis.yml
index 8373798..00f216a 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -12,9 +12,7 @@ env:
     - _R_CHECK_TIMINGS_="0"  ## get the timing information for the examples for all of your functions
 
 r:
- - oldrel
  - release
- - devel
 
 # do not build vignettes...takes too long and times out on travis
 r_build_args: --no-build-vignettes --no-manual
diff --git a/DESCRIPTION b/DESCRIPTION
index d6be4a4..96d0439 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -14,7 +14,7 @@ Imports:
 	caTools,
 	ggplot2,
 	reshape2,
-        gridExtra,
+  gridExtra,
 	rjags,
 	coda,
 	parallel,
@@ -22,6 +22,6 @@ Imports:
 	HiddenMarkov
 RoxygenNote: 6.0.1
 Suggests: 
-        knitr,
-        rmarkdown
+  knitr,
+  rmarkdown
 VignetteBuilder: knitr
diff --git a/NAMESPACE b/NAMESPACE
index 3feeaef..4f66d4e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -24,3 +24,10 @@ export(setMvFit)
 export(summarizeMatrix)
 export(trimSnps)
 exportClasses(HoneyBADGER)
+import(ggplot2)
+import(grDevices)
+import(graphics)
+import(gridExtra)
+import(reshape2)
+import(rjags)
+import(stats)
diff --git a/R/HoneyBADGER.R b/R/HoneyBADGER.R
index de5e268..9d527ba 100644
--- a/R/HoneyBADGER.R
+++ b/R/HoneyBADGER.R
@@ -112,7 +112,9 @@ HoneyBADGER <- setRefClass(
 #' data(gexp)
 #' data(ref)
 #' require(biomaRt) ## for gene coordinates
-#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+#'     dataset = 'hsapiens_gene_ensembl', 
+#'     host = "jul2015.archive.ensembl.org")
 #' hb <- HoneyBADGER$new()
 #' hb$setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 #' 
@@ -200,7 +202,9 @@ HoneyBADGER$methods(
 #' data(gexp)
 #' data(ref)
 #' require(biomaRt) ## for gene coordinates
-#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+#'     dataset = 'hsapiens_gene_ensembl', 
+#'     host = "jul2015.archive.ensembl.org")
 #' hb <- HoneyBADGER$new()
 #' hb$setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 #' hb$plotGexpProfile() 
diff --git a/R/HoneyBADGER_allele.R b/R/HoneyBADGER_allele.R
index 4bb8558..3659d00 100644
--- a/R/HoneyBADGER_allele.R
+++ b/R/HoneyBADGER_allele.R
@@ -7,6 +7,7 @@
 #' @param n.sc.init SNP site coverage count matrix for single cells
 #' @param l.init SNP site alternate allele counts for bulk reference. If NULL, in silico bulk will be created from single cells.
 #' @param n.bulk.init SNP site coverage counts for bulk reference. If NULL, in silico bulk will be created from single cells.
+#' @param filter Whether to filter SNPs to only putative hterozygous SNPs based on the het.deviance.threshold
 #' @param het.deviance.threshold Deviation from expected 0.5 heterozygous fraction 
 #' @param min.cell Minimum number of cells a SNP must have coverage observed in
 #' @param n.cores Number of cores
@@ -162,8 +163,8 @@ setAlleleMats=function(r.init, n.sc.init, l.init=NULL, n.bulk.init=NULL, filter=
 
 
 #' Maps snps to genes
-#'
-#' @name setGeneFactors
+#' 
+#' @param snps SNP annotations
 #' @param txdb TxDb object (ex. TxDb.Hsapiens.UCSC.hg19.knownGene). 
 #' @param fill SNPs mapping to genes not annotated in txdb will be given unique IDs
 #' @param verbose Verbosity
@@ -198,10 +199,10 @@ setGeneFactors=function(snps, txdb, fill=TRUE, verbose=TRUE) {
 
 #' Plot allele profile
 #'
-#' @param r.sub SNP lesser allele count matrix for single cells. If NULL, object's r.maf will be used
-#' @param n.sc.sub SNP coverage count matrix for single cells. If NULL, object's n.sc will be used 
-#' @param l.sub SNP lesser allele count matrix for bulk refernece. If NULL, object's l.maf will be used
-#' @param n.bulk.sub SNP coverage count matrix for bulk refernece. If NULL, object's n.bulk will be used
+#' @param r.maf SNP lesser allele count matrix for single cells.
+#' @param n.sc SNP coverage count matrix for single cells.  
+#' @param l.maf SNP lesser allele count matrix for bulk refernece.
+#' @param n.bulk SNP coverage count matrix for bulk refernece. 
 #' @param snps SNP annotations
 #' @param region Limit plotting to particular GenomicRanges regions
 #' @param chrs Limit plotting to select chromosomes. Default autosomes only. (default: paste0('chr', c(1:22))) 
@@ -215,10 +216,17 @@ setGeneFactors=function(snps, txdb, fill=TRUE, verbose=TRUE) {
 #' data(r)
 #' data(cov.sc)
 #' allele.mats <- setAlleleMats(r, cov.sc)
-#' plotAlleleProfile(allele.mats$r.maf, allele.mats$n.sc, allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, widths=c(249250621, 243199373, 198022430, 191154276, 180915260, 171115067, 159138663, 146364022, 141213431, 135534747, 135006516, 133851895, 115169878, 107349540, 102531392, 90354753, 81195210, 78077248, 59128983, 63025520, 51304566, 48129895)/1e7) 
+#' plotAlleleProfile(allele.mats$r.maf, allele.mats$n.sc, 
+#'     allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, 
+#'     widths=c(249250621, 243199373, 198022430, 191154276, 180915260, 
+#'     171115067, 159138663, 146364022, 141213431, 135534747, 135006516, 
+#'     133851895, 115169878, 107349540, 102531392, 90354753, 81195210, 
+#'     78077248, 59128983, 63025520, 51304566, 48129895)/1e7) 
 #' 
 #' @export
 #' 
+#' @import ggplot2 reshape2 gridExtra stats
+#' 
 plotAlleleProfile=function(r.maf, n.sc, l.maf, n.bulk, snps, region=NULL, chrs=paste0('chr', c(1:22)), widths=NULL, cellOrder=NULL, filter=FALSE, max.ps=3, verbose=FALSE) {
         if(!is.null(region)) {
             overlap <- IRanges::findOverlaps(region, snps)
@@ -282,9 +290,6 @@ plotAlleleProfile=function(r.maf, n.sc, l.maf, n.bulk, snps, region=NULL, chrs=p
         r.tot <- cbind(r.maf/n.sc, 'Bulk'=l.maf/n.bulk)
         n.tot <- cbind(n.sc, 'Bulk'=n.bulk)
         
-        require(ggplot2)
-        require(reshape2)
-        
         if(is.null(widths)) {
             widths <- rep(1, length(chrs))
         } else if (widths[1]=='set'){
@@ -295,11 +300,11 @@ plotAlleleProfile=function(r.maf, n.sc, l.maf, n.bulk, snps, region=NULL, chrs=p
         
         plist <- lapply(chrs, function(chr) {
             vi <- grepl(paste0('^',chr,':'), rownames(r.tot))
-            m <- melt(t(r.tot[vi,]))
+            m <- reshape2::melt(t(r.tot[vi,]))
             colnames(m) <- c('cell', 'snp', 'alt.frac')
             rownames(m) <- paste(m$cell, m$snp)
             m$alt.frac[is.nan(m$alt.frac)] <- NA
-            n <- melt(t(n.tot[vi,]))
+            n <- reshape2::melt(t(n.tot[vi,]))
             colnames(n) <- c('cell', 'snp', 'coverage')
             rownames(n) <- paste(n$cell, n$snp)
             n$coverage[n$coverage>30] <- 30  # max for visualization purposes
@@ -308,27 +313,27 @@ plotAlleleProfile=function(r.maf, n.sc, l.maf, n.bulk, snps, region=NULL, chrs=p
             n$coverage[n$coverage==0] <- NA # if no coverage, just don't show
             dat <- cbind(m, coverage=n$coverage)
             
-            p <- ggplot(dat, aes(snp, cell)) +
+            p <- ggplot2::ggplot(dat, ggplot2::aes(snp, cell)) +
                 ## geom_tile(alpha=0) +
-                geom_point(aes(colour = alt.frac, size = coverage), na.rm=TRUE) +
-                scale_size_continuous(range = c(0, max.ps)) +
+                ggplot2::geom_point(ggplot2::aes(colour = alt.frac, size = coverage), na.rm=TRUE) +
+                ggplot2::scale_size_continuous(range = c(0, max.ps)) +
                 ## scale_colour_gradientn(colours = rainbow(10)) +
-                scale_colour_gradient2(mid="yellow", low = "turquoise", high = "red", midpoint=0.5) +
-                theme(
-                    panel.grid.major = element_blank(), 
-                    panel.grid.minor = element_blank(),
-                    panel.background = element_blank(),
-                    axis.text.x=element_blank(),
-                    axis.title.x=element_blank(),
-                    axis.ticks.x=element_blank(),
-                    axis.text.y=element_blank(),
-                    axis.title.y=element_blank(),
-                    axis.ticks.y=element_blank(),
+                ggplot2::scale_colour_gradient2(mid="yellow", low = "turquoise", high = "red", midpoint=0.5) +
+                ggplot2::theme(
+                    panel.grid.major = ggplot2::element_blank(), 
+                    panel.grid.minor = ggplot2::element_blank(),
+                    panel.background = ggplot2::element_blank(),
+                    axis.text.x=ggplot2::element_blank(),
+                    axis.title.x=ggplot2::element_blank(),
+                    axis.ticks.x=ggplot2::element_blank(),
+                    axis.text.y=ggplot2::element_blank(),
+                    axis.title.y=ggplot2::element_blank(),
+                    axis.ticks.y=ggplot2::element_blank(),
                     legend.position="none",
-                    plot.margin=unit(c(0,0,0,0), "cm"),
-                    panel.border = element_rect(fill = NA, linetype = "solid", colour = "black"),
-                    plot.title = element_text(hjust = 0.5)
-                ) + labs(title = chr)
+                    plot.margin=ggplot2::unit(c(0,0,0,0), "cm"),
+                    panel.border = ggplot2::element_rect(fill = NA, linetype = "solid", colour = "black"),
+                    plot.title = ggplot2::element_text(hjust = 0.5)
+                ) + ggplot2::labs(title = chr)
             ## theme(
             ##     ## axis.text.x=element_text(angle=90,hjust=1,vjust=0.5,size=rel(0.5),lineheight=1),
             ##     ## axis.text.y=element_blank(),
@@ -341,7 +346,6 @@ plotAlleleProfile=function(r.maf, n.sc, l.maf, n.bulk, snps, region=NULL, chrs=p
             return(p)
         })
         
-        require(gridExtra)
         do.call("grid.arrange", c(plist, list(ncol=length(plist), widths=widths)))
 
     }
@@ -349,15 +353,16 @@ plotAlleleProfile=function(r.maf, n.sc, l.maf, n.bulk, snps, region=NULL, chrs=p
 
 #' Calculate posterior probability of CNVs using allele data
 #'
-#' @param r.sub Optional matrix of alt allele count in single cells. If not provided, internal r.sc matrix is used.  
-#' @param n.sub Optional matrix of site coverage count in single cells. If not provided, internal n.sc matrix is used.  
-#' @param l.sub Optional vector of alt allele count in pooled single cells or bulk. If not provided, internal l vector is used.  
-#' @param n.bulk.sub Optional vector of site coverage count in pooled single cells or bulk. If not provided, internal n.bulk vector is used.  
+#' @param r.maf Matrix of alt allele count in single cells.  
+#' @param n.sc Matrix of site coverage count in single cells.  
+#' @param l.maf Vector of alt allele count in pooled single cells or bulk.
+#' @param n.bulk Vector of site coverage count in pooled single cells or bulk. 
+#' @param snps SNP annotations
+#' @param geneFactor Output of \code{\link{setGeneFactors}}
 #' @param region GenomicRanges region of interest such as expected CNV boundaries. 
 #' @param filter Boolean for whether to filter out SNP sites with no coverage. (default: TRUE)
 #' @param pe Effective error rate to capture error from sequencing, etc. (default: 0.01)
 #' @param mono Rate of mono-allelic expression. (default: 0.7)
-#' @param quiet Boolean of whether to suppress progress bar. (default: TRUE)
 #' @param verbose Verbosity(default: FALSE)
 #' 
 #' @examples 
@@ -367,10 +372,14 @@ plotAlleleProfile=function(r.maf, n.sc, l.maf, n.bulk, snps, region=NULL, chrs=p
 #' library(TxDb.Hsapiens.UCSC.hg19.knownGene)
 #' geneFactor <- setGeneFactors(allele.mats$snps, TxDb.Hsapiens.UCSC.hg19.knownGene)
 #' ## test region known to be commonly deleted in glioblastoma
-#' results <- calcAlleleCnvProb(allele.mats$r.maf, allele.mats$n.sc, allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, geneFactor, region=GenomicRanges::GRanges('chr10', IRanges::IRanges(0,1e9)), verbose=TRUE)
+#' results <- calcAlleleCnvProb(allele.mats$r.maf, allele.mats$n.sc, 
+#'     allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, geneFactor, 
+#'     region=GenomicRanges::GRanges('chr10', IRanges::IRanges(0,1e9)), verbose=TRUE)
 #' 
 #' @export
 #' 
+#' @import rjags
+#' 
 calcAlleleCnvProb=function(r.maf, n.sc, l.maf, n.bulk, snps, geneFactor, region=NULL, filter=FALSE, pe=0.1, mono=0.7, verbose=FALSE) {
     quiet = !verbose
     
@@ -479,7 +488,6 @@ calcAlleleCnvProb=function(r.maf, n.sc, l.maf, n.bulk, snps, geneFactor, region=
         if(verbose) {
             cat('Running model ... \n')
         }
-        require(rjags)
         # 4 random chains
         model <- rjags::jags.model(modelFile, data=data, n.chains=4, n.adapt=100, quiet=quiet)
         update(model, 100, progress.bar=ifelse(quiet,"none","text"))
@@ -500,17 +508,18 @@ calcAlleleCnvProb=function(r.maf, n.sc, l.maf, n.bulk, snps, geneFactor, region=
 
 #' Use HMM to identify potential CNV boundaries based on patterns of persistent allelic imbalance
 #' 
-#' @param r.sub Optional matrix of alt allele count in single cells. If not provided, internal r.sc matrix is used.  
-#' @param n.sub Optional matrix of site coverage count in single cells. If not provided, internal n.sc matrix is used.  
-#' @param l.sub Optional vector of alt allele count in pooled single cells or bulk. If not provided, internal l vector is used.  
-#' @param n.bulk.sub Optional vector of site coverage count in pooled single cells or bulk. If not provided, internal n.bulk vector is used.  
+#' @param r.maf Matrix of alt allele count in single cells. 
+#' @param n.sc Matrix of site coverage count in single cells. 
+#' @param l.maf Vector of alt allele count in pooled single cells or bulk.
+#' @param n.bulk Vector of site coverage count in pooled single cells or bulk. 
+#' @param snps SNP annotations
+#' @param geneFactor Output of \code{\link{setGeneFactors}}
 #' @param min.traverse Depth traversal to look for subclonal CNVs. Higher depth, potentially smaller subclones detectable. (default: 3)
 #' @param t HMM transition parameter. Higher number, more transitions. (default: 1e-6)
 #' @param pd Probability of lesser allele detection in deleted region (ie. due to error) 
 #' @param pn Probability of lesser allele detection in neutral region (ie. 0.5 - error rate) 
 #' @param min.num.snps Minimum number of snps in candidate CNV
 #' @param trim Trim boundary SNPs
-#' @param init Boolean whether to initialize
 #' @param verbose Verbosity(default: FALSE)
 #' @param ... Additional parameters to pass to calcAlleleCnvProb
 #' 
@@ -520,13 +529,17 @@ calcAlleleCnvProb=function(r.maf, n.sc, l.maf, n.bulk, snps, geneFactor, region=
 #' allele.mats <- setAlleleMats(r, cov.sc)
 #' library(TxDb.Hsapiens.UCSC.hg19.knownGene)
 #' geneFactor <- setGeneFactors(allele.mats$snps, TxDb.Hsapiens.UCSC.hg19.knownGene)
-#' potentialCnvs <- calcAlleleCnvBoundaries(allele.mats$r.maf, allele.mats$n.sc, allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, geneFactor)
+#' potentialCnvs <- calcAlleleCnvBoundaries(allele.mats$r.maf, allele.mats$n.sc, 
+#'     allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, geneFactor)
 #' ## visualize affected regions
-#' plotAlleleProfile(allele.mats$r.maf, allele.mats$n.sc, allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, region=potentialCnvs$region)
+#' plotAlleleProfile(allele.mats$r.maf, allele.mats$n.sc, allele.mats$l.maf, 
+#'     allele.mats$n.bulk, allele.mats$snps, region=potentialCnvs$region)
 #' }
 #' 
 #' @export
 #' 
+#' @import stats
+#' 
 calcAlleleCnvBoundaries=function(r.maf, n.sc, l.maf, n.bulk, snps, geneFactor, min.traverse=3, t=1e-6, pd=0.1, pn=0.45, min.num.snps=5, trim=0.1, verbose=FALSE, ...) {
   
   snps <- snps[rownames(r.maf)]
diff --git a/R/HoneyBADGER_comb.R b/R/HoneyBADGER_comb.R
index bde378d..5156ad8 100644
--- a/R/HoneyBADGER_comb.R
+++ b/R/HoneyBADGER_comb.R
@@ -5,13 +5,15 @@
 #' 
 #' @export
 #' 
-calcCombCnvProb=function(gexp.norm, genes, mvFit, m=0.15, r.maf, n.sc, l.maf, n.bulk, snps, geneFactor, region=NULL, filter=FALSE, pe=0.1, mono=0.7, n.iter=1000, quiet=FALSE, verbose=FALSE) {
+calcCombCnvProb=function(gexp.norm, genes, mvFit, m=0.15, r.maf, n.sc, l.maf, n.bulk, snps, geneFactor, region=NULL, filter=FALSE, pe=0.1, mono=0.7, verbose=FALSE) {
   
-    geneFactor <- geneFactor[rownames(r.maf)]
-    snps <- snps[rownames(r.maf),]
+  quiet <- !verbose
   
-    genes <- genes[rownames(gexp.norm)]
-    mvFit <- mvFit[colnames(gexp.norm)]
+  geneFactor <- geneFactor[rownames(r.maf)]
+  snps <- snps[rownames(r.maf),]
+  
+  genes <- genes[rownames(gexp.norm)]
+  mvFit <- mvFit[colnames(gexp.norm)]
              
   gexp <- gexp.norm
   gos <- genes
diff --git a/R/HoneyBADGER_gexp.R b/R/HoneyBADGER_gexp.R
index 9c981ab..0cac87c 100644
--- a/R/HoneyBADGER_gexp.R
+++ b/R/HoneyBADGER_gexp.R
@@ -18,7 +18,9 @@
 #' data(gexp)
 #' data(ref)
 #' require(biomaRt) ## for gene coordinates
-#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+#'    dataset = 'hsapiens_gene_ensembl', 
+#'    host = "jul2015.archive.ensembl.org")
 #' gexp.mats <- setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 #' 
 #' @export
@@ -103,6 +105,7 @@ setGexpMats=function(gexp.sc.init, gexp.ref.init, mart.obj, filter=TRUE, minMean
 #' @param gexp.norm Normalized gene expression matrix
 #' @param genes GRanges annotation of gene names and coordinates
 #' @param chrs Chromosomes to be plotted (default: paste0('chr', c(1:22, 'X')))
+#' @param region Optional GenomicRanges region of interest such as expected CNV boundaries. (default: NULL)
 #' @param window.size Window size for sliding window mean. Must be odd number. (default: 101)
 #' @param zlim Limit for plotting heatmap (default: c(-2,2))
 #' @param widths Widths of chromosomes in plot. If 'set' will depend on number of genes in region. Else will be equal.
@@ -112,14 +115,22 @@ setGexpMats=function(gexp.sc.init, gexp.ref.init, mart.obj, filter=TRUE, minMean
 #' data(gexp)
 #' data(ref)
 #' require(biomaRt) ## for gene coordinates
-#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+#'     dataset = 'hsapiens_gene_ensembl', 
+#'     host = "jul2015.archive.ensembl.org")
 #' gexp.mats <- setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 #' ##Set by known chromosome size widths:
 #' ##https://genome.ucsc.edu/goldenpath/help/hg19.chrom.sizes
-#' gexp.plot <- plotGexpProfile(gexp.mats$gexp.norm, gexp.mats$genes, widths=c(249250621, 243199373, 198022430, 191154276, 180915260, 171115067, 159138663, 146364022, 141213431, 135534747, 135006516, 133851895, 115169878, 107349540, 102531392, 90354753, 81195210, 78077248, 59128983, 63025520, 51304566, 48129895)/1e7) 
+#' gexp.plot <- plotGexpProfile(gexp.mats$gexp.norm, gexp.mats$genes, 
+#'     widths=c(249250621, 243199373, 198022430, 191154276, 180915260, 
+#'     171115067, 159138663, 146364022, 141213431, 135534747, 135006516, 
+#'     133851895, 115169878, 107349540, 102531392, 90354753, 81195210, 
+#'     78077248, 59128983, 63025520, 51304566, 48129895)/1e7) 
 #' 
 #' @export
 #' 
+#' @import grDevices graphics stats
+#' 
 plotGexpProfile=function(gexp.norm, genes, chrs=paste0('chr', c(1:22)), region=NULL, window.size=101, zlim=c(-2,2), cellOrder=NULL, widths=NULL) {
         genes <- genes[rownames(gexp.norm)]
 
@@ -211,7 +222,9 @@ plotGexpProfile=function(gexp.norm, genes, chrs=paste0('chr', c(1:22)), region=N
 #' data(gexp)
 #' data(ref)
 #' require(biomaRt) ## for gene coordinates
-#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+#'     dataset = 'hsapiens_gene_ensembl', 
+#'     host = "jul2015.archive.ensembl.org")
 #' gexp.mats <- setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 #' mvFit <- setMvFit(gexp.mats$gexp.norm)
 #' 
@@ -239,10 +252,8 @@ setMvFit=function(gexp.norm, num.genes = seq(5, 100, by=5), rep = 50, plot=FALSE
             if(plot) {
                 par(mfrow=c(1,3), mar=rep(2,4))
                 perf.test <- function(mat) {
-                    require(ggplot2)
-                    require(reshape2)
-                    m <- melt(mat)
-                    p <- ggplot(m) + geom_boxplot(aes(x = factor(Var2), y = value))
+                    m <- reshape2::melt(mat)
+                    p <- ggplot2::ggplot(m) + ggplot2::geom_boxplot(ggplot2::aes(x = factor(Var2), y = value))
                     return(p)
                 }
                 perf.test(mean.comp)
@@ -290,7 +301,9 @@ setMvFit=function(gexp.norm, num.genes = seq(5, 100, by=5), rep = 50, plot=FALSE
 #' data(gexp)
 #' data(ref)
 #' require(biomaRt) ## for gene coordinates
-#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+#'     dataset = 'hsapiens_gene_ensembl', 
+#'     host = "jul2015.archive.ensembl.org")
 #' gexp.mats <- setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 #' dev <- setGexpDev(gexp.mats$gexp.norm)
 #' 
@@ -333,13 +346,18 @@ setGexpDev=function(gexp.norm, alpha=0.25, n=100, seed=0, plot=FALSE, verbose=FA
 #' data(gexp)
 #' data(ref)
 #' require(biomaRt) ## for gene coordinates
-#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+#' mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+#'     dataset = 'hsapiens_gene_ensembl', 
+#'     host = "jul2015.archive.ensembl.org")
 #' gexp.mats <- setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 #' mvFit <- setMvFit(gexp.mats$gexp.norm)
-#' results <- calcGexpCnvProb(gexp.mats$gexp.norm, gexp.mats$genes, mvFit, region=GenomicRanges::GRanges('chr10', IRanges::IRanges(0,1e9)), verbose=TRUE)
+#' results <- calcGexpCnvProb(gexp.mats$gexp.norm, gexp.mats$genes, 
+#'     mvFit, region=GenomicRanges::GRanges('chr10', IRanges::IRanges(0,1e9)), verbose=TRUE)
 #' 
 #' @export
 #' 
+#' @import rjags
+#' 
 calcGexpCnvProb=function(gexp.norm, genes, mvFit, m=0.15, region=NULL, verbose=FALSE) {
         gexp <- gexp.norm
         gos <- genes[rownames(gexp.norm)]
@@ -400,8 +418,7 @@ calcGexpCnvProb=function(gexp.norm, genes, mvFit, m=0.15, region=NULL, verbose=F
             list(S = rep(0, ncol(gexp)), dd = 1),
             list(S = rep(1, ncol(gexp)), dd = 1)
         )
-        require(rjags)
-        model <- jags.model(modelFile, data=data, inits=inits, n.chains=4, n.adapt=100, quiet=quiet)
+        model <- rjags::jags.model(modelFile, data=data, inits=inits, n.chains=4, n.adapt=100, quiet=quiet)
         update(model, 100, progress.bar=ifelse(quiet,"none","text"))
         
         parameters <- c('S', 'dd')
@@ -430,15 +447,18 @@ calcGexpCnvProb=function(gexp.norm, genes, mvFit, m=0.15, region=NULL, verbose=F
 #' 
 #' @param gexp.norm Normalized gene expression matrix
 #' @param genes GRanges annotation of gene names and coordinates
+#' @param m Expression magnitude deviation needed to distinguish CNV from neutral
 #' @param chrs List of chromosome names. Genes not mapping to these chromosomes will be excluded. Default autosomes only: paste0('chr', c(1:22))
 #' @param min.traverse Depth traversal to look for subclonal CNVs. Higher depth, potentially smaller subclones detectable. (default: 2)
 #' @param min.num.genes Minimum number of genes within a CNV. (default: 3)
+#' @param trim Trim boundary SNPs
 #' @param t HMM transition parameter. Higher number, more transitions. (default: 1e-6)
-#' @param init Initialize recursion (default: FALSE)
 #' @param verbose Verbosity (default: FALSE)
 #' 
 #' @export
 #' 
+#' @import stats
+#' 
 calcGexpCnvBoundaries=function(gexp.norm, genes, m=0.15, chrs=paste0('chr', c(1:22)), min.traverse=3, t=1e-6, min.num.genes=3, trim=0.1, verbose=FALSE) {
 
         genes <- genes[rownames(gexp.norm)]
diff --git a/R/prepareData_cxue.R b/R/clonal_deletion_detection.R
similarity index 99%
rename from R/prepareData_cxue.R
rename to R/clonal_deletion_detection.R
index f087cc5..526277d 100644
--- a/R/prepareData_cxue.R
+++ b/R/clonal_deletion_detection.R
@@ -1,5 +1,5 @@
 #' @author Catherine Xue
-#' Functions for assessing clonal deletions
+#' Functions for assessing for clonal deletions
 
 
 #' Helper function to get total allele counts and coverage across all cells at each 
diff --git a/R/prepareData.R b/R/prepareData.R
index 6d1ee4f..d68eb6e 100644
--- a/R/prepareData.R
+++ b/R/prepareData.R
@@ -29,7 +29,7 @@
 #' }
 #'
 #' @export
-#'
+#' 
 getAlleleCount <- function (gr, bamFile, indexFile, verbose = FALSE) {
   df <- data.frame(seqnames(gr), ranges(gr))
   names <- paste(df$seqnames.gr., paste(df$start, df$end, sep='-'), sep=':')
@@ -94,7 +94,7 @@ getAlleleCount <- function (gr, bamFile, indexFile, verbose = FALSE) {
 #' }
 #'
 #' @export
-#'
+#' 
 getCellAlleleCount <- function (gr, bamFile, indexFile, cellBarcodes, verbose = FALSE, n.cores=1) {
   df <- data.frame(seqnames(gr), ranges(gr))
   names <- paste(df$seqnames.gr., paste(df$start, df$end, sep='-'), sep=':')
@@ -310,6 +310,8 @@ getSnpMats <- function(snps, bamFiles, indexFiles, n.cores=1, verbose=FALSE) {
 #' @param indexFiles list of bai index file
 #' @param n.cores number of cores
 #' @param verbose Boolean of whether or not to print progress and info
+#' @param barcodes Cell barcodes 
+#' 
 #' @return
 #'   refCount reference allele count matrix for each cell and each position of interest
 #'   altCount alternative allele count matrix for each cell and each position of interest
diff --git a/man/HoneyBADGER_plotGexpProfile.Rd b/man/HoneyBADGER_plotGexpProfile.Rd
index 87a1ec5..0140c28 100644
--- a/man/HoneyBADGER_plotGexpProfile.Rd
+++ b/man/HoneyBADGER_plotGexpProfile.Rd
@@ -27,7 +27,9 @@ Plot gene expression profile
 data(gexp)
 data(ref)
 require(biomaRt) ## for gene coordinates
-mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+    dataset = 'hsapiens_gene_ensembl', 
+    host = "jul2015.archive.ensembl.org")
 hb <- HoneyBADGER$new()
 hb$setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 hb$plotGexpProfile() 
diff --git a/man/HoneyBADGER_setGexpMats.Rd b/man/HoneyBADGER_setGexpMats.Rd
index 4e34161..a8180f9 100644
--- a/man/HoneyBADGER_setGexpMats.Rd
+++ b/man/HoneyBADGER_setGexpMats.Rd
@@ -31,7 +31,9 @@ Set gene expression matrices, normalizes, and maps genes to genomic coordinates
 data(gexp)
 data(ref)
 require(biomaRt) ## for gene coordinates
-mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+    dataset = 'hsapiens_gene_ensembl', 
+    host = "jul2015.archive.ensembl.org")
 hb <- HoneyBADGER$new()
 hb$setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 
diff --git a/man/calcAlleleCnvBoundaries.Rd b/man/calcAlleleCnvBoundaries.Rd
index 40adfbd..509e90e 100644
--- a/man/calcAlleleCnvBoundaries.Rd
+++ b/man/calcAlleleCnvBoundaries.Rd
@@ -9,6 +9,18 @@ calcAlleleCnvBoundaries(r.maf, n.sc, l.maf, n.bulk, snps, geneFactor,
   trim = 0.1, verbose = FALSE, ...)
 }
 \arguments{
+\item{r.maf}{Matrix of alt allele count in single cells.}
+
+\item{n.sc}{Matrix of site coverage count in single cells.}
+
+\item{l.maf}{Vector of alt allele count in pooled single cells or bulk.}
+
+\item{n.bulk}{Vector of site coverage count in pooled single cells or bulk.}
+
+\item{snps}{SNP annotations}
+
+\item{geneFactor}{Output of \code{\link{setGeneFactors}}}
+
 \item{min.traverse}{Depth traversal to look for subclonal CNVs. Higher depth, potentially smaller subclones detectable. (default: 3)}
 
 \item{t}{HMM transition parameter. Higher number, more transitions. (default: 1e-6)}
@@ -24,16 +36,6 @@ calcAlleleCnvBoundaries(r.maf, n.sc, l.maf, n.bulk, snps, geneFactor,
 \item{verbose}{Verbosity(default: FALSE)}
 
 \item{...}{Additional parameters to pass to calcAlleleCnvProb}
-
-\item{r.sub}{Optional matrix of alt allele count in single cells. If not provided, internal r.sc matrix is used.}
-
-\item{n.sub}{Optional matrix of site coverage count in single cells. If not provided, internal n.sc matrix is used.}
-
-\item{l.sub}{Optional vector of alt allele count in pooled single cells or bulk. If not provided, internal l vector is used.}
-
-\item{n.bulk.sub}{Optional vector of site coverage count in pooled single cells or bulk. If not provided, internal n.bulk vector is used.}
-
-\item{init}{Boolean whether to initialize}
 }
 \description{
 Use HMM to identify potential CNV boundaries based on patterns of persistent allelic imbalance
@@ -45,9 +47,11 @@ data(cov.sc)
 allele.mats <- setAlleleMats(r, cov.sc)
 library(TxDb.Hsapiens.UCSC.hg19.knownGene)
 geneFactor <- setGeneFactors(allele.mats$snps, TxDb.Hsapiens.UCSC.hg19.knownGene)
-potentialCnvs <- calcAlleleCnvBoundaries(allele.mats$r.maf, allele.mats$n.sc, allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, geneFactor)
+potentialCnvs <- calcAlleleCnvBoundaries(allele.mats$r.maf, allele.mats$n.sc, 
+    allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, geneFactor)
 ## visualize affected regions
-plotAlleleProfile(allele.mats$r.maf, allele.mats$n.sc, allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, region=potentialCnvs$region)
+plotAlleleProfile(allele.mats$r.maf, allele.mats$n.sc, allele.mats$l.maf, 
+    allele.mats$n.bulk, allele.mats$snps, region=potentialCnvs$region)
 }
 
 }
diff --git a/man/calcAlleleCnvProb.Rd b/man/calcAlleleCnvProb.Rd
index 9f39bc5..e1985d2 100644
--- a/man/calcAlleleCnvProb.Rd
+++ b/man/calcAlleleCnvProb.Rd
@@ -8,25 +8,27 @@ calcAlleleCnvProb(r.maf, n.sc, l.maf, n.bulk, snps, geneFactor, region = NULL,
   filter = FALSE, pe = 0.1, mono = 0.7, verbose = FALSE)
 }
 \arguments{
-\item{region}{GenomicRanges region of interest such as expected CNV boundaries.}
+\item{r.maf}{Matrix of alt allele count in single cells.}
 
-\item{filter}{Boolean for whether to filter out SNP sites with no coverage. (default: TRUE)}
+\item{n.sc}{Matrix of site coverage count in single cells.}
 
-\item{pe}{Effective error rate to capture error from sequencing, etc. (default: 0.01)}
+\item{l.maf}{Vector of alt allele count in pooled single cells or bulk.}
 
-\item{mono}{Rate of mono-allelic expression. (default: 0.7)}
+\item{n.bulk}{Vector of site coverage count in pooled single cells or bulk.}
 
-\item{verbose}{Verbosity(default: FALSE)}
+\item{snps}{SNP annotations}
 
-\item{r.sub}{Optional matrix of alt allele count in single cells. If not provided, internal r.sc matrix is used.}
+\item{geneFactor}{Output of \code{\link{setGeneFactors}}}
 
-\item{n.sub}{Optional matrix of site coverage count in single cells. If not provided, internal n.sc matrix is used.}
+\item{region}{GenomicRanges region of interest such as expected CNV boundaries.}
 
-\item{l.sub}{Optional vector of alt allele count in pooled single cells or bulk. If not provided, internal l vector is used.}
+\item{filter}{Boolean for whether to filter out SNP sites with no coverage. (default: TRUE)}
 
-\item{n.bulk.sub}{Optional vector of site coverage count in pooled single cells or bulk. If not provided, internal n.bulk vector is used.}
+\item{pe}{Effective error rate to capture error from sequencing, etc. (default: 0.01)}
 
-\item{quiet}{Boolean of whether to suppress progress bar. (default: TRUE)}
+\item{mono}{Rate of mono-allelic expression. (default: 0.7)}
+
+\item{verbose}{Verbosity(default: FALSE)}
 }
 \description{
 Calculate posterior probability of CNVs using allele data
@@ -38,6 +40,8 @@ allele.mats <- setAlleleMats(r, cov.sc)
 library(TxDb.Hsapiens.UCSC.hg19.knownGene)
 geneFactor <- setGeneFactors(allele.mats$snps, TxDb.Hsapiens.UCSC.hg19.knownGene)
 ## test region known to be commonly deleted in glioblastoma
-results <- calcAlleleCnvProb(allele.mats$r.maf, allele.mats$n.sc, allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, geneFactor, region=GenomicRanges::GRanges('chr10', IRanges::IRanges(0,1e9)), verbose=TRUE)
+results <- calcAlleleCnvProb(allele.mats$r.maf, allele.mats$n.sc, 
+    allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, geneFactor, 
+    region=GenomicRanges::GRanges('chr10', IRanges::IRanges(0,1e9)), verbose=TRUE)
 
 }
diff --git a/man/calcCombCnvProb.Rd b/man/calcCombCnvProb.Rd
index 706c5fc..1e6b04e 100644
--- a/man/calcCombCnvProb.Rd
+++ b/man/calcCombCnvProb.Rd
@@ -6,7 +6,7 @@
 \usage{
 calcCombCnvProb(gexp.norm, genes, mvFit, m = 0.15, r.maf, n.sc, l.maf, n.bulk,
   snps, geneFactor, region = NULL, filter = FALSE, pe = 0.1, mono = 0.7,
-  n.iter = 1000, quiet = FALSE, verbose = FALSE)
+  verbose = FALSE)
 }
 \arguments{
 \item{gexp.norm}{Normalized gene expression matrix.}
@@ -17,6 +17,18 @@ calcCombCnvProb(gexp.norm, genes, mvFit, m = 0.15, r.maf, n.sc, l.maf, n.bulk,
 
 \item{m}{Expression deviation due to copy number change (default: 0.15)}
 
+\item{r.maf}{Matrix of alt allele count in single cells.}
+
+\item{n.sc}{Matrix of site coverage count in single cells.}
+
+\item{l.maf}{Vector of alt allele count in pooled single cells or bulk.}
+
+\item{n.bulk}{Vector of site coverage count in pooled single cells or bulk.}
+
+\item{snps}{SNP annotations}
+
+\item{geneFactor}{Output of \code{\link{setGeneFactors}}}
+
 \item{region}{Optional GenomicRanges region of interest such as expected CNV boundaries. (default: NULL)}
 
 \item{filter}{Boolean for whether to filter out SNP sites with no coverage. (default: TRUE)}
@@ -25,8 +37,6 @@ calcCombCnvProb(gexp.norm, genes, mvFit, m = 0.15, r.maf, n.sc, l.maf, n.bulk,
 
 \item{mono}{Rate of mono-allelic expression. (default: 0.7)}
 
-\item{quiet}{Boolean of whether to suppress progress bar. (default: TRUE)}
-
 \item{verbose}{Verbosity (default: FALSE)}
 }
 \description{
diff --git a/man/calcGexpCnvBoundaries.Rd b/man/calcGexpCnvBoundaries.Rd
index 300be83..e051f1c 100644
--- a/man/calcGexpCnvBoundaries.Rd
+++ b/man/calcGexpCnvBoundaries.Rd
@@ -13,6 +13,8 @@ calcGexpCnvBoundaries(gexp.norm, genes, m = 0.15, chrs = paste0("chr",
 
 \item{genes}{GRanges annotation of gene names and coordinates}
 
+\item{m}{Expression magnitude deviation needed to distinguish CNV from neutral}
+
 \item{chrs}{List of chromosome names. Genes not mapping to these chromosomes will be excluded. Default autosomes only: paste0('chr', c(1:22))}
 
 \item{min.traverse}{Depth traversal to look for subclonal CNVs. Higher depth, potentially smaller subclones detectable. (default: 2)}
@@ -21,9 +23,9 @@ calcGexpCnvBoundaries(gexp.norm, genes, m = 0.15, chrs = paste0("chr",
 
 \item{min.num.genes}{Minimum number of genes within a CNV. (default: 3)}
 
-\item{verbose}{Verbosity (default: FALSE)}
+\item{trim}{Trim boundary SNPs}
 
-\item{init}{Initialize recursion (default: FALSE)}
+\item{verbose}{Verbosity (default: FALSE)}
 }
 \description{
 HMM to identify CNV boundaries using normalized gene expression data
diff --git a/man/calcGexpCnvProb.Rd b/man/calcGexpCnvProb.Rd
index 50c70a1..da39164 100644
--- a/man/calcGexpCnvProb.Rd
+++ b/man/calcGexpCnvProb.Rd
@@ -27,9 +27,12 @@ Calculate posterior probability of CNVs using normalized expression data
 data(gexp)
 data(ref)
 require(biomaRt) ## for gene coordinates
-mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+    dataset = 'hsapiens_gene_ensembl', 
+    host = "jul2015.archive.ensembl.org")
 gexp.mats <- setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 mvFit <- setMvFit(gexp.mats$gexp.norm)
-results <- calcGexpCnvProb(gexp.mats$gexp.norm, gexp.mats$genes, mvFit, region=GenomicRanges::GRanges('chr10', IRanges::IRanges(0,1e9)), verbose=TRUE)
+results <- calcGexpCnvProb(gexp.mats$gexp.norm, gexp.mats$genes, 
+    mvFit, region=GenomicRanges::GRanges('chr10', IRanges::IRanges(0,1e9)), verbose=TRUE)
 
 }
diff --git a/man/getHetRate.Rd b/man/getHetRate.Rd
index ff04b69..fb37319 100644
--- a/man/getHetRate.Rd
+++ b/man/getHetRate.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/prepareData_cxue.R
+% Please edit documentation in R/clonal_deletion_detection.R
 \name{getHetRate}
 \alias{getHetRate}
 \title{Helper function to get het rate from allele counts at a list of common variant
diff --git a/man/getSnpMats10X.Rd b/man/getSnpMats10X.Rd
index f1278c6..dfd1b98 100644
--- a/man/getSnpMats10X.Rd
+++ b/man/getSnpMats10X.Rd
@@ -14,6 +14,8 @@ getSnpMats10X(snps, bamFiles, indexFiles, barcodes, n.cores = 1,
 
 \item{indexFiles}{list of bai index file}
 
+\item{barcodes}{Cell barcodes}
+
 \item{n.cores}{number of cores}
 
 \item{verbose}{Boolean of whether or not to print progress and info}
diff --git a/man/isHet.Rd b/man/isHet.Rd
index f0ac9a7..444a3b9 100644
--- a/man/isHet.Rd
+++ b/man/isHet.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/prepareData_cxue.R
+% Please edit documentation in R/clonal_deletion_detection.R
 \name{isHet}
 \alias{isHet}
 \title{Helper function to determine heterzogosity at a position from allele counts 
diff --git a/man/isHetAlt.Rd b/man/isHetAlt.Rd
index 1394dae..5632e10 100644
--- a/man/isHetAlt.Rd
+++ b/man/isHetAlt.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/prepareData_cxue.R
+% Please edit documentation in R/clonal_deletion_detection.R
 \name{isHetAlt}
 \alias{isHetAlt}
 \title{Alternate helper function to determine heterzogosity at a position from allele  
diff --git a/man/plotAlleleProfile.Rd b/man/plotAlleleProfile.Rd
index ee4eafe..0f47101 100644
--- a/man/plotAlleleProfile.Rd
+++ b/man/plotAlleleProfile.Rd
@@ -9,6 +9,14 @@ plotAlleleProfile(r.maf, n.sc, l.maf, n.bulk, snps, region = NULL,
   filter = FALSE, max.ps = 3, verbose = FALSE)
 }
 \arguments{
+\item{r.maf}{SNP lesser allele count matrix for single cells.}
+
+\item{n.sc}{SNP coverage count matrix for single cells.}
+
+\item{l.maf}{SNP lesser allele count matrix for bulk refernece.}
+
+\item{n.bulk}{SNP coverage count matrix for bulk refernece.}
+
 \item{snps}{SNP annotations}
 
 \item{region}{Limit plotting to particular GenomicRanges regions}
@@ -24,14 +32,6 @@ plotAlleleProfile(r.maf, n.sc, l.maf, n.bulk, snps, region = NULL,
 \item{max.ps}{Maximum point size for plot.}
 
 \item{verbose}{Verbosity}
-
-\item{r.sub}{SNP lesser allele count matrix for single cells. If NULL, object's r.maf will be used}
-
-\item{n.sc.sub}{SNP coverage count matrix for single cells. If NULL, object's n.sc will be used}
-
-\item{l.sub}{SNP lesser allele count matrix for bulk refernece. If NULL, object's l.maf will be used}
-
-\item{n.bulk.sub}{SNP coverage count matrix for bulk refernece. If NULL, object's n.bulk will be used}
 }
 \description{
 Plot allele profile
@@ -40,6 +40,11 @@ Plot allele profile
 data(r)
 data(cov.sc)
 allele.mats <- setAlleleMats(r, cov.sc)
-plotAlleleProfile(allele.mats$r.maf, allele.mats$n.sc, allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, widths=c(249250621, 243199373, 198022430, 191154276, 180915260, 171115067, 159138663, 146364022, 141213431, 135534747, 135006516, 133851895, 115169878, 107349540, 102531392, 90354753, 81195210, 78077248, 59128983, 63025520, 51304566, 48129895)/1e7) 
+plotAlleleProfile(allele.mats$r.maf, allele.mats$n.sc, 
+    allele.mats$l.maf, allele.mats$n.bulk, allele.mats$snps, 
+    widths=c(249250621, 243199373, 198022430, 191154276, 180915260, 
+    171115067, 159138663, 146364022, 141213431, 135534747, 135006516, 
+    133851895, 115169878, 107349540, 102531392, 90354753, 81195210, 
+    78077248, 59128983, 63025520, 51304566, 48129895)/1e7) 
 
 }
diff --git a/man/plotGexpProfile.Rd b/man/plotGexpProfile.Rd
index b398116..947ad4c 100644
--- a/man/plotGexpProfile.Rd
+++ b/man/plotGexpProfile.Rd
@@ -15,6 +15,8 @@ plotGexpProfile(gexp.norm, genes, chrs = paste0("chr", c(1:22)),
 
 \item{chrs}{Chromosomes to be plotted (default: paste0('chr', c(1:22, 'X')))}
 
+\item{region}{Optional GenomicRanges region of interest such as expected CNV boundaries. (default: NULL)}
+
 \item{window.size}{Window size for sliding window mean. Must be odd number. (default: 101)}
 
 \item{zlim}{Limit for plotting heatmap (default: c(-2,2))}
@@ -30,10 +32,16 @@ Plot gene expression profile
 data(gexp)
 data(ref)
 require(biomaRt) ## for gene coordinates
-mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+    dataset = 'hsapiens_gene_ensembl', 
+    host = "jul2015.archive.ensembl.org")
 gexp.mats <- setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 ##Set by known chromosome size widths:
 ##https://genome.ucsc.edu/goldenpath/help/hg19.chrom.sizes
-gexp.plot <- plotGexpProfile(gexp.mats$gexp.norm, gexp.mats$genes, widths=c(249250621, 243199373, 198022430, 191154276, 180915260, 171115067, 159138663, 146364022, 141213431, 135534747, 135006516, 133851895, 115169878, 107349540, 102531392, 90354753, 81195210, 78077248, 59128983, 63025520, 51304566, 48129895)/1e7) 
+gexp.plot <- plotGexpProfile(gexp.mats$gexp.norm, gexp.mats$genes, 
+    widths=c(249250621, 243199373, 198022430, 191154276, 180915260, 
+    171115067, 159138663, 146364022, 141213431, 135534747, 135006516, 
+    133851895, 115169878, 107349540, 102531392, 90354753, 81195210, 
+    78077248, 59128983, 63025520, 51304566, 48129895)/1e7) 
 
 }
diff --git a/man/setAlleleMats.Rd b/man/setAlleleMats.Rd
index 0d01492..4549114 100644
--- a/man/setAlleleMats.Rd
+++ b/man/setAlleleMats.Rd
@@ -18,6 +18,8 @@ setAlleleMats(r.init, n.sc.init, l.init = NULL, n.bulk.init = NULL,
 
 \item{n.bulk.init}{SNP site coverage counts for bulk reference. If NULL, in silico bulk will be created from single cells.}
 
+\item{filter}{Whether to filter SNPs to only putative hterozygous SNPs based on the het.deviance.threshold}
+
 \item{het.deviance.threshold}{Deviation from expected 0.5 heterozygous fraction}
 
 \item{min.cell}{Minimum number of cells a SNP must have coverage observed in}
diff --git a/man/setGeneFactors.Rd b/man/setGeneFactors.Rd
index 7a4c40d..084ba80 100644
--- a/man/setGeneFactors.Rd
+++ b/man/setGeneFactors.Rd
@@ -7,6 +7,8 @@
 setGeneFactors(snps, txdb, fill = TRUE, verbose = TRUE)
 }
 \arguments{
+\item{snps}{SNP annotations}
+
 \item{txdb}{TxDb object (ex. TxDb.Hsapiens.UCSC.hg19.knownGene).}
 
 \item{fill}{SNPs mapping to genes not annotated in txdb will be given unique IDs}
diff --git a/man/setGexpDev.Rd b/man/setGexpDev.Rd
index 1068e50..3b9a467 100644
--- a/man/setGexpDev.Rd
+++ b/man/setGexpDev.Rd
@@ -27,7 +27,9 @@ Set needed absolute gene expression deviance to be able to distinguish neutral f
 data(gexp)
 data(ref)
 require(biomaRt) ## for gene coordinates
-mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+    dataset = 'hsapiens_gene_ensembl', 
+    host = "jul2015.archive.ensembl.org")
 gexp.mats <- setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 dev <- setGexpDev(gexp.mats$gexp.norm)
 
diff --git a/man/setGexpMats.Rd b/man/setGexpMats.Rd
index bc414e3..647ae83 100644
--- a/man/setGexpMats.Rd
+++ b/man/setGexpMats.Rd
@@ -39,7 +39,9 @@ Set gene expression matrices, normalizes, and maps genes to genomic coordinates
 data(gexp)
 data(ref)
 require(biomaRt) ## for gene coordinates
-mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+   dataset = 'hsapiens_gene_ensembl', 
+   host = "jul2015.archive.ensembl.org")
 gexp.mats <- setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 
 }
diff --git a/man/setMvFit.Rd b/man/setMvFit.Rd
index fffb37b..c485e28 100644
--- a/man/setMvFit.Rd
+++ b/man/setMvFit.Rd
@@ -25,7 +25,9 @@ Model expected gene expression variance as a function of number of genes
 data(gexp)
 data(ref)
 require(biomaRt) ## for gene coordinates
-mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
+mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", 
+    dataset = 'hsapiens_gene_ensembl', 
+    host = "jul2015.archive.ensembl.org")
 gexp.mats <- setGexpMats(gexp, ref, mart.obj, filter=FALSE, scale=FALSE)
 mvFit <- setMvFit(gexp.mats$gexp.norm)
 
diff --git a/man/trimSnps.Rd b/man/trimSnps.Rd
index 93a2b99..b330fee 100644
--- a/man/trimSnps.Rd
+++ b/man/trimSnps.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/prepareData_cxue.R
+% Please edit documentation in R/clonal_deletion_detection.R
 \name{trimSnps}
 \alias{trimSnps}
 \title{Function to filter out SNPs from ExAC database}