diff --git a/R/Dcenter.R b/R/Dcenter.R
index 1d03a62..9b57af7 100644
--- a/R/Dcenter.R
+++ b/R/Dcenter.R
@@ -24,5 +24,6 @@ Dcenter <- function(x) {
   # normalise a
   ahat <- a - matrix(rowMeans(a), nrow = n, ncol = n) - matrix(colMeans(a), nrow = n, ncol = n, byrow = TRUE) + mean(a)
 
+  #return(ahat[!upper.tri(ahat)])
   return(ahat)
 }
diff --git a/R/dpm.R b/R/dpm.R
index 56acd57..14e4eaf 100644
--- a/R/dpm.R
+++ b/R/dpm.R
@@ -2,16 +2,26 @@
 #'
 #' @param data is a matrix containing samples in rows, variables in columns
 #'
-#' @return a matrix with score for every edge
+#' @return a nvar*nvar matrix containing reg.DPM scores for all links (edges) where nvar is the number of variables
 #'
 #' @import corpcor
 #'
 #' @examples
-#' ## Generate non-linear data
-#' data <- nldata(100,0.2)$data
+#' Example1:
+#' ## Generate non-linear data with 100 samples (from the network discussed in the paper) where the gaussian noise with standard deviation of sigma=0.2 is added to the generated data
+#' data <- nldata(nsamp=100,sigma=0.2)$data
 #'
-#'## run DPM
-#' dpm(data)
+#'## run regularized DPM
+#' res <-reg.dpm(data)
+#' head(res)
+#'
+#' Example2:
+#' ##load DREAM4 data (insilico_size10_1_knockouts)
+#' data(dream4)
+#'
+#'## Run regularized DPM
+#' res <- reg.dpm(dream4)
+#' head(res)
 #'
 #' @export
 
@@ -20,14 +30,10 @@
 ###############################
 
 dpm <- function(data) {
-  # data is a matrix containing samples in rows, variables in columns
-
   # compute the double-centred vectors of each variable and concatenate them into the columns of matrix d
   d <- apply(as.matrix(data), 2, Dcenter)
-
   # compute the precision matrix of d (cor2pcor is a function from corpcor Library)
   res <- corpcor::cor2pcor(cov(d))
-
   return(res)
 }
 
diff --git a/R/dream4_data.R b/R/dream4_data.R
new file mode 100644
index 0000000..3f6dc66
--- /dev/null
+++ b/R/dream4_data.R
@@ -0,0 +1,16 @@
+#' One of the expression data (insilico_size10_1_knockouts) from DREAM4 in-silico challenge
+#'
+#'
+#' @docType data
+#'
+#' @usage data(dream4)
+#'
+#' @format gene expression data as a matrix where columns correspond to 10 genes and rows correspond to samples (10 knockout).
+#'
+#' @keywords datasets
+#'
+#' @examples
+#' data(dream4)
+#' reg.dpm(dream4)
+#'
+"dream4"
diff --git a/R/dream4_gs.R b/R/dream4_gs.R
new file mode 100644
index 0000000..66d3f05
--- /dev/null
+++ b/R/dream4_gs.R
@@ -0,0 +1,14 @@
+#' The gold standard of insilico_size10 data from DREAM4 in-silico challenge
+#'
+#' @docType data
+#'
+#' @usage data(dream4gs)
+#'
+#' @format the adjacency matrix where columns and rows correspond 10 genes and the entry 1 in the matrix indicates a link between two corresponding genes.
+#'
+#' @keywords datasets
+#'
+#' @examples
+#' data(dream4gs)
+#'
+"dream4gs"
diff --git a/R/fisher_test.R b/R/fisher_test.R
index f27faf6..d5c72fc 100644
--- a/R/fisher_test.R
+++ b/R/fisher_test.R
@@ -1,21 +1,28 @@
-#' Function to compute fisher z transformation pvalues
+#' Function to compute fisher z-transformation pvalues for the links
 #'
-#' @param p a
-#' @param alpha a
-#' @param nsamp is the number of required samples
-#' @param nnodes is the number of nodes in the simulated graph
+#' @param score_matrix score matrix obtained from DPM or reg.DPM.
+#' @param alpha a significance level
+#' @param nsamp is the number of samples (number of rows of input matrix for (reg-)DPM ). Note that we need nsamp such that: nsamp*(nsamp+1)> 2*(nvar-5)
+#' @param nvar is the number of variables (number of columns of input matrix for (reg-)DPM )
 #'
-#' @return a list contains the pvalues (pval) and correspending decisions of having a significant link (isnotnull) for all links
+#' @return a list containing
+#' - the list of significant links as a data frame. Each line of the data frame corresponds to a link.
+#' The first and second columns are the corresponding nodes of the link, the third column is the score of the link and the forth column is the corresponding pvalue.
+#' - the threshold that all the links with scores more than it are significant at defined significance level.
 #'
 #' @examples
-#' ## Generate non-linear data
-#' data <- nldata(100,0.2)$data
+#' ## Load DREAM4 data (insilico_size10_1_knockouts)
+#' data(dream4)
 #'
 #'## Run regularized DPM
 #' res <- reg.dpm(data)
 #'
-#' ## Compute pvalues
-#' fisher_test(res, 0.05,)
+#' ## Get the list of significant links at significance level alpha=0.05
+#' fisher_test(res,alpha=0.05,10,10)$LinkList
+#'
+#' ##Get the threshold at significance level alpha=0.05
+#' fisher_test(score_matrix=res,alpha=0.05,nsamp=10,nvar=10)$threshold
+#'
 #'
 #' @export
 
@@ -23,15 +30,22 @@
 ### Function to compute fisher z transformation pvalues ###
 ###########################################################
 
-fisher_test <- function(p, alpha, nsamp, nnodes){
-  z = sqrt(nsamp-(nnodes-2)-3) * 0.5 * abs(log((1 + p) / (1 - p)))
-  pval = 2 * pnorm(-z)
-  pval_tr = qnorm(1-alpha/2)
-  isnotnull = (z > pval_tr )
-  temp = exp( 2 * pval_tr / (sqrt(nsamp-(nnodes-2)-3)) )
-  threshold = (temp -1)/(temp + 1)
-  return(list(pval=pval, isnotnull=isnotnull,threshold=threshold))
+fisher_test <- function(score_matrix, alpha, nsamp, nvar){
+  nsamp <- (nsamp*(nsamp+1))/2
+  if (nsamp-(nvar-2)-3 <= 0 ) stop("the test requires nsamp such that: nsamp*(nsamp+1)> 2*(nvar-5)")
+  diag(score_matrix) <- NA
+  score_matrix[upper.tri(score_matrix)] <- NA
+  linkList <- reshape2::melt(score_matrix, na.rm=TRUE)
+  colnames(linkList) <- c("Node1", "Node2", "Score")
+  p <- linkList["Score"]
+  z <- sqrt(nsamp-(nvar-2)-3) * abs(atan(p))
+  pval <- 2 * pnorm(-as.matrix(z))
+  colnames(pval) <- "Pvalue"
+  linkList <- cbind(linkList,pval)
+  linkList <- linkList[linkList$Pvalue<alpha,]
+  temp <- exp( 2 * pval_tr / (sqrt(nsamp-(nvar-2)-3)) )
+  threshold <- (temp -1)/(temp + 1)
+  return(list(LinkList=linkList,threshold=threshold))
 }
 
 
-
diff --git a/R/get_link.R b/R/get_link.R
index 5b0908b..5024f54 100644
--- a/R/get_link.R
+++ b/R/get_link.R
@@ -7,23 +7,24 @@
 #' @return List of links in a data frame. Each line of the data frame corresponds to a link. The first and second columns are the corresponding nodes of the link and the third column is the score of the link.
 #'
 #' @examples
-#'## Generate linear data with 10 nodes and 100 samples
-#' data <- rdata(nnodes=10,nsamp=100)$data
-#' rownames(data) <- paste("Sample", 1:100, sep="")
-#' colnames(data) <- paste("Node", 1:10, sep="")
+#'##load DREAM4 data (insilico_size10_1_knockouts)
+#' data(dream4)
 #'
-#' ## Run reg_DPM
-#' score_mat <- reg.dpm(data)
+#'## Run regularized DPM
+#' score_mat <- reg.dpm(dream4)
 #'
-#' ## Get ranking of link
+#' ## Get ranking of links
 #' links <- get_link(score_mat)
 #' head(linkList)
 #'
-#' ## Get only top ranked links
+#' ## Get only the top ranked links
 #' links <- get_link(score_mat,top_ranked=10)
+#' head(linkList)
 #'
 #' ## Get only the links with a score higher than a threshold
 #' links <- get_link(score_mat,threshold = 0.2)
+#' head(linkList)
+#'
 #' @export
 #'
 #'
diff --git a/R/reg.dpm.R b/R/reg.dpm.R
index 2d411ae..3066ef2 100644
--- a/R/reg.dpm.R
+++ b/R/reg.dpm.R
@@ -1,18 +1,27 @@
 #' Function to compute regularized DPM
 #'
-#' @param data is a matrix containing samples in rows, variables in columns
+#' @param data is a matrix containing samples in rows and variables in columns
 #'
-#' @return a matrix with score for every edge
+#' @return a nvar*nvar matrix containing reg.DPM scores for all links (edges) where nvar is the number of variables
 #'
 #' @import corpcor
 #'
 #' @examples
-#' ## Generate non-linear data with 100 samples and the standard deviation of 0.2 of the noise added to the generated data
+#' example1:
+#' ## Generate non-linear data with 100 samples (from the network discussed in the paper) where the gaussian noise with standard deviation of sigma=0.2 is added to the generated data
 #' data <- nldata(nsamp=100,sigma=0.2)$data
 #'
 #'## run regularized DPM
 #' reg.dpm(data)
 #'
+#' example2:
+#' ##load DREAM4 data
+#' data(dream4)
+#'
+#'## Run regularized DPM
+#' res <- reg.dpm(data)
+#'
+#'
 #' @export
 
 
@@ -21,13 +30,10 @@
 ###########################################
 
 reg.dpm <- function(data) {
-  # data is a matrix containing samples in rows, variables in columns
-
   # compute the double-centred vectors of each variable and concatenate them into the columns of matrix d
   d <- apply(as.matrix(data), 2, Dcenter)
-
   # compute the regularised precision matrix of d
   res <- corpcor::pcor.shrink(d)
-
+  res <- matrix(res,nrow(res),ncol(res))
   return(res)
 }
diff --git a/README.md b/README.md
index 6a99f23..9a0ad34 100644
--- a/README.md
+++ b/README.md
@@ -17,49 +17,32 @@ library(devtools)
 devtools::install_github("ghanbari/DPM",host="github.molgen.mpg.de/api/v3")
 ```
 
-### Simulation of data
-#### linear data
-To simulate linear data you need to first install the necessary R packages from CRAN:
+### Format of input data
+The (reg.)DPM function takes as input argument a matrix DataMat where each column of the matrix corresponds to a variable and each row corresponds to a sample. The column names of the DataMat specify the names of the variables. You can also specify the name of samples as rownames but this is not necessary. For example, the following code generate a fake data matrix:
 
 ```r
-install.packages(c("pcalg","mvtnorm","qpgraph"))
+DataMat <- matrix(sample(1:10, 200, replace=TRUE), ncol=10, nrow=20)
+colnames(DataMat) <- paste("variable", 1:10, sep="")
+rownames(DataMat) <- paste("Sample", 1:20, sep="")
+head(DataMat)
 ```
-Then you can simulate linear data by specifying your desired number of nodes (`nnodes`) and number of samples (`nsamp`).
-
-```r
-library(DPM)
-ldata <- rdata(nnodes=10,nsamp=100)[["data"]]
-```
-
-#### non-linear data
-To simulate non-linear data from the network mentioned in the paper with your desired number of samples (`nsamp`) and the standard deviation of the noise added to the generated data (`sigma`) use:
+In the context of gene networks variables correspond to genes. The input is gene expression data as a matrix in which columns correspond to genes and rows correspond to samples. As an example, we have one data from DREAM4 challenge (insilico_size10_1_knockouts):
 
 ```r
-library(DPM)
-nldata <- nldata(nsamp=100,sigma=0.2)[["data"]]
+data(dream4)
+head(dream4)
 ```
 
 ### Computing DPM and reg-DPM
-The following example computes DPM and reg-DPM scores for nonlinear data sampled from the network mentioned in the paper. You can use your own data as a matrix containing samples in rows and variables in columns.
+The following example computes DPM and reg-DPM scores for dream4 data. As previously discussed, you can use your own data as a matrix containing samples in rows and variables in columns.
 
 ```r
 library(DPM)
-data <- nldata(nsamp=100,sigma=0.2)[["data"]]
-dpmres <- dpm(data)
-regdpmres <- reg.dpm(data)
+data(dream4)
+dpmres <- dpm(dream4)
+regdpmres <- reg.dpm(dream4)
 ```
 
-You can plot the distribution of DPM and reg-DPM scores using:
-
-```r
-library("ggplot2")
-dpmdat <- data.frame(score = abs(dpmres[lower.tri(dpmres)]),name ='DPM')
-regdpmdat <-data.frame(score = abs( regdpmres[lower.tri(regdpmres)]), name='reg-DPM')
-dat <- rbind(dpmdat,regdpmdat)
-
-ggplot(dat, aes(x=score,color=name))+ theme_bw()  + geom_density(size=1.5)+scale_color_manual(values=c("DPM"="SteelBlue3","reg-DPM"="dodgerblue4"))
-ggsave(“test.png”, plot = last_plot(), device = “png”)
-```
 
 ### Visualization of the network
 
@@ -87,10 +70,48 @@ cut value is automatically chosen to be equal to the maximum of the 75th quantil
 
 ```r
 library(qgraph)
-qgraph(matrix(dpmres,nrow(dpmres),ncol(dpmres)), layout = "spring" , minimum=0.25, cut=0.55, filename="dpm-network", filetype="png")
-qgraph(matrix(regdpmres,nrow(regdpmres),ncol(regdpmres)), layout = "spring" , minimum=0.25, cut=0.55, filename="regdpm-network", filetype="png")
+qgraph(dpmres, layout = "spring" , minimum=0.25, cut=0.55, filename="dpm-network", filetype="png")
+qgraph(regdpmres, layout = "spring" , minimum=0.25, cut=0.55, filename="regdpm-network", filetype="png")
+```
+
+### Threshold selection
+We recommend you to look at the distribution of the scores and based on that decide for a threshold. You can plot the distribution of DPM and reg-DPM scores using:
+
+```r
+library("ggplot2")
+dpmdat <- data.frame(score = abs(dpmres[lower.tri(dpmres)]),name ='DPM')
+regdpmdat <-data.frame(score = abs( regdpmres[lower.tri(regdpmres)]), name='reg-DPM')
+dat <- rbind(dpmdat,regdpmdat)
+
+ggplot(dat, aes(x=score,color=name))+ theme_bw()  + geom_density(size=1.5)+scale_color_manual(values=c("DPM"="SteelBlue3","reg-DPM"="dodgerblue4"))
+ggsave(“ScoreDistribution.png”, plot = last_plot(), device = “png”)
 ```
 
+When you decide for the threshold you can visualize the resulted network (see section [Visualization of the network](### Visualization of the network
+) ) and get all the links with a score higher than the threshold (see subsection [Get only the links with a score higher than a threshold](####Get only the links with a score higher than a threshold) )
+
+
+However, we provide a threshold selection method based on the [fisher z-transformation](https://en.wikipedia.org/wiki/Fisher_transformation). You can compute the threshold for the DREAM4 data with the following command where:
+
+* `score_matrix` is the score matrix obtained from DPM or reg.DPM.
+* `alpha` is a significance value
+* `nsamp` is the number of samples (number of rows of input matrix for (reg-)DPM )
+* `nvar` is the number of variables (number of columns of input matrix for (reg-)DPM )
+
+```r
+fisher_test(dpmres, alpha=0.05, nsamp=nrow(dream4), nnodes=ncol(dream4))$threshold
+fisher_test(regdpmres, alpha=0.05, nsamp=nrow(dream4), nnodes=ncol(dream4))$threshold
+```
+
+You can also get all the significant links based on fisher z-transformation at significance level alpha:
+```r
+fisher_test(dpmres, alpha=0.05, nsamp=nrow(dream4), nnodes=ncol(dream4))$Listlink
+fisher_test(regdpmres, alpha=0.05, nsamp=nrow(dream4), nnodes=ncol(dream4))$Listlink
+```
+
+As an alternative, you can select the top-ranked links based on (reg.)DPM score which are most likely to be true links.
+See section [Get only the top-ranked links](####get only the top-ranked links)
+
 ### Get the list of the links
 You need to first install the necessary R packages from CRAN:
 
@@ -103,6 +124,7 @@ install.packages("reshape2")
 You can get the list of all links (from the highest to the lowest score) with the following command, where the resulting dpm/reddpm_list contains the ranking of links. Each row corresponds to a link. The first and second columns show the corresponding nodes and the third column indicates the score of the link.
 
 ```{r}
+library(reshape2)
 dpm_links <- get_link(dpmres)
 dim(dpm_links)
 head(dpm_links)
@@ -112,6 +134,18 @@ dim(regdpm_links)
 head(regdpm_links)
 ```
 
+#### Get only the links with a score higher than a threshold
+If you are interested in extracting the links with scores higher than a threshold use the parameter `threshold` and specify your threshold:
+
+```r
+dpm_links <- get_link(dpmres,threshold = 0.2)
+dim(dpm_links)
+head(dpm_links)
+
+regdpm_links <- get_link(regdpmres,threshold = 0.2)
+dim(regdpm_links)
+head(regdpm_links)
+```
 #### Get only the top-ranked links
 
 If you are interested in extracting the top links with the highest scores, use the parameter `top_ranked` and specify the number of top-ranked links to report:
@@ -126,15 +160,29 @@ dim(regdpm_links)
 head(regdpm_links)
 ```
 
-#### Get only the links with a score higher than a threshold
-If you are interested in extracting the links with scores higher than a threshold use the parameter `threshold` and specify your threshold:
+### Simulation of data
+#### linear data
+To simulate linear data you need to first install the necessary R packages from CRAN:
 
 ```r
-dpm_links <- get_link(dpmres,threshold = 0.2)
-dim(dpm_links)
-head(dpm_links)
+install.packages(c("pcalg","mvtnorm","qpgraph"))
+```
+Then you can simulate linear data by specifying your desired number of nodes (`nnodes`) and number of samples (`nsamp`).
 
-regdpm_links <- get_link(regdpmres,threshold = 0.2)
-dim(regdpm_links)
-head(regdpm_links)
+```r
+library(DPM)
+data <- rdata(nnodes=10,nsamp=100)[["data"]]
+dpmres <- dpm(data)
+regdpmres <- reg.dpm(data)
+```
+
+
+#### non-linear data
+To simulate non-linear data from the network mentioned in the paper with your desired number of samples (`nsamp`) and the standard deviation of the noise added to the generated data (`sigma`) use:
+
+```r
+library(DPM)
+data <- nldata(nsamp=100,sigma=0.2)[["data"]]
+dpmres <- dpm(data)
+regdpmres <- reg.dpm(data)
 ```
diff --git a/data/dream4.rda b/data/dream4.rda
new file mode 100644
index 0000000..3bfae59
Binary files /dev/null and b/data/dream4.rda differ
diff --git a/data/dream4gs.rda b/data/dream4gs.rda
new file mode 100644
index 0000000..359d449
Binary files /dev/null and b/data/dream4gs.rda differ
diff --git a/man/dpm.Rd b/man/dpm.Rd
index 2741b56..f0ca119 100644
--- a/man/dpm.Rd
+++ b/man/dpm.Rd
@@ -10,16 +10,26 @@ dpm(data)
 \item{data}{is a matrix containing samples in rows, variables in columns}
 }
 \value{
-a matrix with score for every edge
+a nvar*nvar matrix containing reg.DPM scores for all links (edges) where nvar is the number of variables
 }
 \description{
 Function to compute DPM
 }
 \examples{
-## Generate non-linear data
-data <- nldata(100,0.2)$data
+Example1:
+## Generate non-linear data with 100 samples (from the network discussed in the paper) where the gaussian noise with standard deviation of sigma=0.2 is added to the generated data
+data <- nldata(nsamp=100,sigma=0.2)$data
 
-## run DPM
-dpm(data)
+## run regularized DPM
+res <-reg.dpm(data)
+head(res)
+
+Example2:
+##load DREAM4 data (insilico_size10_1_knockouts)
+data(dream4)
+
+## Run regularized DPM
+res <- reg.dpm(dream4)
+head(res)
 
 }
diff --git a/man/dream4.Rd b/man/dream4.Rd
new file mode 100644
index 0000000..6dc086e
--- /dev/null
+++ b/man/dream4.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/dream4_data.R
+\docType{data}
+\name{dream4}
+\alias{dream4}
+\title{One of the expression data (insilico_size10_1_knockouts) from DREAM4 in-silico challenge}
+\format{gene expression data as a matrix where columns correspond to 10 genes and rows correspond to samples (10 knockout).}
+\usage{
+data(dream4)
+}
+\description{
+One of the expression data (insilico_size10_1_knockouts) from DREAM4 in-silico challenge
+}
+\examples{
+data(dream4)
+reg.dpm(dream4)
+
+}
+\keyword{datasets}
diff --git a/man/dream4gs.Rd b/man/dream4gs.Rd
new file mode 100644
index 0000000..52623bf
--- /dev/null
+++ b/man/dream4gs.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/dream4_gs.R
+\docType{data}
+\name{dream4gs}
+\alias{dream4gs}
+\title{The gold standard of insilico_size10 data from DREAM4 in-silico challenge}
+\format{the adjacency matrix where columns and rows correspond 10 genes and the entry 1 in the matrix indicates a link between two corresponding genes.}
+\usage{
+data(dream4gs)
+}
+\description{
+The gold standard of insilico_size10 data from DREAM4 in-silico challenge
+}
+\examples{
+data(dream4gs)
+
+}
+\keyword{datasets}
diff --git a/man/fisher_test.Rd b/man/fisher_test.Rd
index 4d29372..6d4a87f 100644
--- a/man/fisher_test.Rd
+++ b/man/fisher_test.Rd
@@ -2,33 +2,40 @@
 % Please edit documentation in R/fisher_test.R
 \name{fisher_test}
 \alias{fisher_test}
-\title{Function to compute fisher z transformation pvalues}
+\title{Function to compute fisher z-transformation pvalues for the links}
 \usage{
-fisher_test(p, alpha, nsamp, nnodes)
+fisher_test(score_matrix, alpha, nsamp, nvar)
 }
 \arguments{
-\item{p}{a}
+\item{score_matrix}{score matrix obtained from DPM or reg.DPM.}
 
-\item{alpha}{a}
+\item{alpha}{a significance level}
 
-\item{nsamp}{is the number of required samples}
+\item{nsamp}{is the number of samples (number of rows of input matrix for (reg-)DPM ). Note that we need nsamp such that: nsamp*(nsamp+1)> 2*(nvar-5)}
 
-\item{nnodes}{is the number of nodes in the simulated graph}
+\item{nvar}{is the number of variables (number of columns of input matrix for (reg-)DPM )}
 }
 \value{
-a list contains the pvalues (pval) and correspending decisions of having a significant link (isnotnull) for all links
+a list containing
+- the list of significant links as a data frame. Each line of the data frame corresponds to a link.
+The first and second columns are the corresponding nodes of the link, the third column is the score of the link and the forth column is the corresponding pvalue.
+- the threshold that all the links with scores more than it are significant at defined significance level.
 }
 \description{
-Function to compute fisher z transformation pvalues
+Function to compute fisher z-transformation pvalues for the links
 }
 \examples{
-## Generate non-linear data
-data <- nldata(100,0.2)$data
+## Load DREAM4 data (insilico_size10_1_knockouts)
+data(dream4)
 
 ## Run regularized DPM
 res <- reg.dpm(data)
 
-## Compute pvalues
-fisher_test(res, 0.05,)
+## Get the list of significant links at significance level alpha=0.05
+fisher_test(res,alpha=0.05,10,10)$LinkList
+
+##Get the threshold at significance level alpha=0.05
+fisher_test(score_matrix=res,alpha=0.05,nsamp=10,nvar=10)$threshold
+
 
 }
diff --git a/man/get_link.Rd b/man/get_link.Rd
index 2e85f34..aa975df 100644
--- a/man/get_link.Rd
+++ b/man/get_link.Rd
@@ -20,21 +20,22 @@ List of links in a data frame. Each line of the data frame corresponds to a link
 Converts the score matrix (obtained from DPM or reg-DPM) to a sorted list of non-directed links (links with higher score first).
 }
 \examples{
-## Generate linear data with 10 nodes and 100 samples
-data <- rdata(nnodes=10,nsamp=100)$data
-rownames(data) <- paste("Sample", 1:100, sep="")
-colnames(data) <- paste("Node", 1:10, sep="")
+##load DREAM4 data (insilico_size10_1_knockouts)
+data(dream4)
 
-## Run reg_DPM
-score_mat <- reg.dpm(data)
+## Run regularized DPM
+score_mat <- reg.dpm(dream4)
 
-## Get ranking of link
+## Get ranking of links
 links <- get_link(score_mat)
 head(linkList)
 
-## Get only top ranked links
+## Get only the top ranked links
 links <- get_link(score_mat,top_ranked=10)
+head(linkList)
 
 ## Get only the links with a score higher than a threshold
 links <- get_link(score_mat,threshold = 0.2)
+head(linkList)
+
 }
diff --git a/man/reg.dpm.Rd b/man/reg.dpm.Rd
index ff5040a..d877cd6 100644
--- a/man/reg.dpm.Rd
+++ b/man/reg.dpm.Rd
@@ -7,19 +7,28 @@
 reg.dpm(data)
 }
 \arguments{
-\item{data}{is a matrix containing samples in rows, variables in columns}
+\item{data}{is a matrix containing samples in rows and variables in columns}
 }
 \value{
-a matrix with score for every edge
+a nvar*nvar matrix containing reg.DPM scores for all links (edges) where nvar is the number of variables
 }
 \description{
 Function to compute regularized DPM
 }
 \examples{
-## Generate non-linear data with 100 samples and the standard deviation of 0.2 of the noise added to the generated data
+example1:
+## Generate non-linear data with 100 samples (from the network discussed in the paper) where the gaussian noise with standard deviation of sigma=0.2 is added to the generated data
 data <- nldata(nsamp=100,sigma=0.2)$data
 
 ## run regularized DPM
 reg.dpm(data)
 
+example2:
+##load DREAM4 data
+data(dream4)
+
+## Run regularized DPM
+res <- reg.dpm(data)
+
+
 }