diff --git a/DESCRIPTION b/DESCRIPTION
index ae683a44..14f222ec 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: netZooR
 Type: Package
 Title: Unified methods for the inference and analysis of gene regulatory networks
-Version: 1.3.25
+Version: 1.3.28
 Date: 2023-03-27
 Authors@R: c(person("Marouen", "Ben Guebila",
 			email = "benguebila@hsph.harvard.edu", role = c("aut","cre"), comment = c(ORCID = "0000-0001-5934-966X")),
diff --git a/NAMESPACE b/NAMESPACE
index 99745492..851d8dcc 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,5 +1,8 @@
 # Generated by roxygen2: do not edit by hand
 
+export(TIGER)
+export(adj2el)
+export(adj2regulon)
 export(alpaca)
 export(alpacaCrane)
 export(alpacaExtractTopGenes)
@@ -15,6 +18,8 @@ export(craneUnipartite)
 export(createCondorObject)
 export(createPandaStyle)
 export(dragon)
+export(el2adj)
+export(el2regulon)
 export(elistToAdjMat)
 export(lioness)
 export(lionessPy)
@@ -36,6 +41,8 @@ export(pandaDiffEdges)
 export(pandaPy)
 export(pandaToAlpaca)
 export(pandaToCondorObject)
+export(prior.pp)
+export(puma)
 export(runEgret)
 export(sambar)
 export(sourcePPI)
diff --git a/R/PUMA.R b/R/PUMA.R
index 27f92eb1..f0f1549d 100644
--- a/R/PUMA.R
+++ b/R/PUMA.R
@@ -45,8 +45,9 @@
 #' "coopNet" is the cooperative network which is not updated for miRNAs
 #' @examples
 #' data(pandaToyData)
+#' mirs = c("AHR","AR","ARID3A","ARNT","BRCA1","CEBPA","CREB1","DDIT3")
 #' pumaRes <- puma(pandaToyData$motif,
-#'            pandaToyData$expression,NULL,hamming=.1,progress=TRUE)
+#'            pandaToyData$expression,NULL,mir_file=mirs,hamming=.1,progress=TRUE)
 #' @references
 #' Kuijjer, Marieke L., et al. "PUMA: PANDA using microRNA associations." Bioinformatics 36.18 (2020): 4765-4773.
 puma <- function(motif,expr=NULL,ppi=NULL,alpha=0.1,mir_file,hamming=0.001,
@@ -238,11 +239,11 @@ puma <- function(motif,expr=NULL,ppi=NULL,alpha=0.1,mir_file,hamming=0.001,
     rownames(tfCoopNetwork) <- tf.names
   }
   
-  if(mir_file != NULL){
+  if(!is.null(mir_file)){
     mirIndex = match(mir_file,tf.names)
     tfCoopNetwork[mirIndex,] = 0
     tfCoopNetwork[,mirIndex] = 0
-    seqs = seq(1, num.tfs*num.tfs, num.tfs+1)
+    seqs = seq(1, num.TFs*num.TFs, num.TFs+1)
     tfCoopNetwork[seqs] <- 1
     # tfCoopNetwork has now a diagonal of 1 and all entries are zeros 
     # for miRNA-miRNA interactions and TF-miRNA interactions
@@ -288,7 +289,7 @@ puma <- function(motif,expr=NULL,ppi=NULL,alpha=0.1,mir_file,hamming=0.001,
     geneCoreg=minusAlpha*geneCoreg + alpha*CoReg2
     
     #PUMA step to skip update of PPI matrix for miRNA interactions
-    seqs = seq(1, num.tfs*num.tfs, num.tfs+1)
+    seqs = seq(1, num.TFs*num.TFs, num.TFs+1)
     savediag = tfCoopNetwork[seqs] # save diagonal
     tfCoopNetwork[mirIndex,] <- TFCoopInit[mirIndex,]
     tfCoopNetwork[,mirIndex] <- TFCoopInit[,mirIndex]
@@ -422,4 +423,4 @@ prepResult <- function(zScale, output, regulatoryNetwork, geneCoreg, tfCoopNetwo
 }
 
 pandaObj <- setClass("panda", slots=c("regNet","coregNet","coopNet","numGenes","numTFs","numEdges"))
-setMethod("show","panda",function(object){print.panda(object)})
+setMethod("show","panda",function(object){print.panda(object)})
\ No newline at end of file
diff --git a/R/SPIDER.R b/R/SPIDER.R
index 1ecb043d..e593819e 100644
--- a/R/SPIDER.R
+++ b/R/SPIDER.R
@@ -50,8 +50,8 @@
 #' pandaToyData$epifilter = pandaToyData$motif
 #' nind=floor(runif(5000, min=1, max=dim(pandaToyData$epifilter)[1]))
 #' pandaToyData$epifilter[nind,3] = 0
-#' #spiderRes <- spider(pandaToyData$motif,pandaToyData$expression,
-#' #                    pandaToyData$epifilter,pandaToyData$ppi,hamming=.1,progress=TRUE)
+#' spiderRes <- spider(pandaToyData$motif,pandaToyData$expression,
+#'                     pandaToyData$epifilter,pandaToyData$ppi,hamming=.1,progress=TRUE)
 #' @references
 #' Sonawane, Abhijeet Rajendra, et al. "Constructing gene regulatory networks using epigenetic data." npj Systems Biology and Applications 7.1 (2021): 1-13.
 spider <- function(motif,expr=NULL,epifilter=NULL,ppi=NULL,alpha=0.1,hamming=0.001,
diff --git a/man/TIGER.Rd b/man/TIGER.Rd
new file mode 100644
index 00000000..a759fa09
--- /dev/null
+++ b/man/TIGER.Rd
@@ -0,0 +1,71 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/TIGER.R
+\name{TIGER}
+\alias{TIGER}
+\title{TIGER main function}
+\usage{
+TIGER(
+  expr,
+  prior,
+  method = "VB",
+  signed = TRUE,
+  baseline = TRUE,
+  psis_loo = FALSE,
+  seed = 123,
+  out_path = NULL,
+  out_size = 300,
+  a_sigma = 1,
+  b_sigma = 1,
+  a_alpha = 1,
+  b_alpha = 1,
+  sigmaZ = 10,
+  sigmaB = 1
+)
+}
+\arguments{
+\item{expr}{A normalized log-transformed gene expressison matrix. Rows are genes
+and columns are sampeles (cells).}
+
+\item{prior}{A prior regulatory network in adjacency matrix format. Rows are TFs
+and columns target genes.}
+
+\item{method}{Method used for Bayesian inference. "VB" or "MCMC". Defaults to "VB".}
+
+\item{signed}{Prior network is signed or not. Defaults to TRUE.}
+
+\item{baseline}{Include baseline or not. Defaults to TRUE.}
+
+\item{psis_loo}{Use pareto smoothed importance sampling leave-one-out cross
+validation to check model fitting or not. Defaults to FALSE.}
+
+\item{seed}{Seed for reproducible results. Defaults to 123.}
+
+\item{out_path}{(Optional) output path for CmdStanVB or CmdStanMCMC object. Defaults to NULL.}
+
+\item{out_size}{Posterior sampling size. Default = 300.}
+
+\item{a_sigma}{Hyperparameter of error term. Default = 1.}
+
+\item{b_sigma}{Hyperparameter of error term. Default = 1.}
+
+\item{a_alpha}{Hyperparameter of edge weight W. Default = 1.}
+
+\item{b_alpha}{Hyperparameter of edge weight W. Default = 1.}
+
+\item{sigmaZ}{Standard deviation of TF activity Z. Default = 10.}
+
+\item{sigmaB}{Standard deviation of baseline term. Default = 1.}
+}
+\value{
+A TIGER list object.
+* W is the estimated regulatory network, but different from prior network,
+rows are genes and columns are TFs.
+* Z is the estimated TF activities, rows are TFs and columns are samples.
+* TF.name, TG.name, and sample.name are the used TFs, target genes and samples.
+* If psis_loo is TRUE, loocv is a table of psis_loo result for model checking.
+* If psis_loo is TRUE, elpd_loo is the Bayesian LOO estimate of the expected log pointwise predictive
+density, which can be used for Bayesian stacking to handle multi-modality later.
+}
+\description{
+TIGER main function
+}
diff --git a/man/adj2el.Rd b/man/adj2el.Rd
new file mode 100644
index 00000000..67baa31a
--- /dev/null
+++ b/man/adj2el.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/TIGER.R
+\name{adj2el}
+\alias{adj2el}
+\title{Convert a bipartite adjacency matrix to an edgelist}
+\usage{
+adj2el(adj)
+}
+\arguments{
+\item{adj}{An adjacency matrix, with rows as TFs and columns as genes.}
+}
+\value{
+An edge list dataframe with three columns. First column is TF name,
+second column is gene name, and third column is edge weight.
+}
+\description{
+Convert a bipartite adjacency matrix to an edgelist
+}
diff --git a/man/adj2regulon.Rd b/man/adj2regulon.Rd
new file mode 100644
index 00000000..a549b7f5
--- /dev/null
+++ b/man/adj2regulon.Rd
@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/TIGER.R
+\name{adj2regulon}
+\alias{adj2regulon}
+\title{Convert bipartite adjacency to regulon}
+\usage{
+adj2regulon(adj)
+}
+\arguments{
+\item{adj}{An adjacency matrix, with rows as TFs and columns as genes.}
+}
+\value{
+A VIPER required regulon object.
+}
+\description{
+Convert bipartite adjacency to regulon
+}
diff --git a/man/el2adj.Rd b/man/el2adj.Rd
new file mode 100644
index 00000000..351355fb
--- /dev/null
+++ b/man/el2adj.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/TIGER.R
+\name{el2adj}
+\alias{el2adj}
+\title{Convert bipartite edge list to adjacency mat}
+\usage{
+el2adj(el)
+}
+\arguments{
+\item{el}{An edge list dataframe with three columns. First column is TF name,
+second column is gene name, and third column is edge weight.}
+}
+\value{
+An adjacency matrix with rows as TFs and columns as genes.
+}
+\description{
+Convert bipartite edge list to adjacency mat
+}
diff --git a/man/el2regulon.Rd b/man/el2regulon.Rd
new file mode 100644
index 00000000..ee9b7e48
--- /dev/null
+++ b/man/el2regulon.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/TIGER.R
+\name{el2regulon}
+\alias{el2regulon}
+\title{Convert a bipartite edgelist to regulon}
+\usage{
+el2regulon(el)
+}
+\arguments{
+\item{el}{An edge list dataframe with three columns. First column is TF name,
+second column is gene name, and third column is edge weight.}
+}
+\value{
+A VIPER required regulon object
+}
+\description{
+Convert a bipartite edgelist to regulon
+}
diff --git a/man/pandaPy.Rd b/man/pandaPy.Rd
index 95a10fa8..790852ee 100644
--- a/man/pandaPy.Rd
+++ b/man/pandaPy.Rd
@@ -41,7 +41,7 @@ Also, this can be generated with a list of proteins of interest by \code{\link{s
 
 \item{remove_missing}{Only when modeProcess='legacy': remove_missing='TRUE' removes all unmatched TF and genes; remove_missing='FALSE' keeps all tf and genes. The default value is 'FALSE'.}
 
-\item{with_header}{if TRUE reads header of expression matrix}
+\item{with_header}{Boolean to read gene expression file with a header for sample names}
 }
 \value{
 When save_memory=FALSE(default), this function will return a list of three items: 
diff --git a/man/prior.pp.Rd b/man/prior.pp.Rd
new file mode 100644
index 00000000..04719523
--- /dev/null
+++ b/man/prior.pp.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/TIGER.R
+\name{prior.pp}
+\alias{prior.pp}
+\title{Filter low confident edge signs in the prior network using GeneNet}
+\usage{
+prior.pp(prior, expr)
+}
+\arguments{
+\item{prior}{A prior network (adjacency matrix) with rows as TFs and columns as genes.}
+
+\item{expr}{A normalized log-transformed gene expression matrix.}
+}
+\value{
+A filtered prior network (adjacency matrix).
+}
+\description{
+Filter low confident edge signs in the prior network using GeneNet
+}
diff --git a/man/puma.Rd b/man/puma.Rd
new file mode 100644
index 00000000..c8b4422a
--- /dev/null
+++ b/man/puma.Rd
@@ -0,0 +1,96 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/PUMA.R
+\name{puma}
+\alias{puma}
+\title{PANDA using microRNA associations}
+\usage{
+puma(
+  motif,
+  expr = NULL,
+  ppi = NULL,
+  alpha = 0.1,
+  mir_file,
+  hamming = 0.001,
+  iter = NA,
+  output = c("regulatory", "coexpression", "cooperative"),
+  zScale = TRUE,
+  progress = FALSE,
+  randomize = c("None", "within.gene", "by.gene"),
+  cor.method = "pearson",
+  scale.by.present = FALSE,
+  edgelist = FALSE,
+  remove.missing.ppi = FALSE,
+  remove.missing.motif = FALSE,
+  remove.missing.genes = FALSE,
+  mode = "union"
+)
+}
+\arguments{
+\item{motif}{A miRNA target dataset, a data.frame, matrix or exprSet containing 3 columns.
+Each row describes the association between a miRNA (column 1) its target
+gene (column 2) and a score (column 3) for the association from TargetScan or miRanda}
+
+\item{expr}{An expression dataset, as a genes (rows) by samples (columns) data.frame}
+
+\item{ppi}{This can be set to 1) NULL which will be encoded as an identity matrix between miRNAs in PUMA for now. 
+Or 2) it can include a set of TF interactions, or 3) a mix of TFs and miRNAs.}
+
+\item{alpha}{value to be used for update variable, alpha (default=0.1)}
+
+\item{mir_file}{list of miRNA to filter the PPI matrix and prevent update of miRNA edges.}
+
+\item{hamming}{value at which to terminate the process based on hamming distance (default 10^-3)}
+
+\item{iter}{sets the maximum number of iterations PUMA can run before exiting.}
+
+\item{output}{a vector containing which networks to return.  Options include "regulatory",
+"coregulatory", "cooperative".}
+
+\item{zScale}{Boolean to indicate use of z-scores in output.  False will use [0,1] scale.}
+
+\item{progress}{Boolean to indicate printing of output for algorithm progress.}
+
+\item{randomize}{method by which to randomize gene expression matrix.  Default "None".  Must
+be one of "None", "within.gene", "by.genes".  "within.gene" randomization scrambles each row
+of the gene expression matrix, "by.gene" scrambles gene labels.}
+
+\item{cor.method}{Correlation method, default is "pearson".}
+
+\item{scale.by.present}{Boolean to indicate scaling of correlations by percentage of positive samples.}
+
+\item{edgelist}{Boolean to indicate if edge lists instead of matrices should be returned.}
+
+\item{remove.missing.ppi}{Boolean to indicate whether miRNAs in the PPI but not in the motif data should be
+removed. Only when mode=='legacy'.}
+
+\item{remove.missing.motif}{Boolean to indicate whether genes targeted in the motif data but not the
+expression data should be removed. Only when mode=='legacy'.}
+
+\item{remove.missing.genes}{Boolean to indicate whether genes in the expression data but lacking
+information from the motif prior should be removed. Only when mode=='legacy'.}
+
+\item{mode}{The data alignment mode. The mode 'union' takes the union of the genes in the expression matrix and the motif
+and the union of TFs in the ppi and motif and fills the matrics with zeros for nonintersecting TFs and gens, 'intersection' 
+takes the intersection of genes and TFs and removes nonintersecting sets, 'legacy' is the old behavior with version 1.19.3.
+#' Parameters remove.missing.ppi, remove.missingmotif, remove.missing.genes work only with mode=='legacy'.}
+}
+\value{
+An object of class "panda" containing matrices describing networks achieved by convergence
+with PUMA algorithm.\cr
+"regNet" is the regulatory network\cr
+"coregNet" is the coregulatory network\cr
+"coopNet" is the cooperative network which is not updated for miRNAs
+}
+\description{
+This function runs the PUMA algorithm to predict a miRNA-gene regulatory network
+}
+\examples{
+data(pandaToyData)
+mirs = c("AHR","AR","ARID3A","ARNT","BRCA1","CEBPA","CREB1","DDIT3")
+pumaRes <- puma(pandaToyData$motif,
+           pandaToyData$expression,NULL,mir_file=mirs,hamming=.1,progress=TRUE)
+}
+\references{
+Kuijjer, Marieke L., et al. "PUMA: PANDA using microRNA associations." Bioinformatics 36.18 (2020): 4765-4773.
+}
+\keyword{keywords}
diff --git a/man/spider.Rd b/man/spider.Rd
index ec9a3282..3f1a45f2 100644
--- a/man/spider.Rd
+++ b/man/spider.Rd
@@ -91,8 +91,8 @@ data(pandaToyData)
 pandaToyData$epifilter = pandaToyData$motif
 nind=floor(runif(5000, min=1, max=dim(pandaToyData$epifilter)[1]))
 pandaToyData$epifilter[nind,3] = 0
-#spiderRes <- spider(pandaToyData$motif,pandaToyData$expression,
-#                    pandaToyData$epifilter,pandaToyData$ppi,hamming=.1,progress=TRUE)
+spiderRes <- spider(pandaToyData$motif,pandaToyData$expression,
+                    pandaToyData$epifilter,pandaToyData$ppi,hamming=.1,progress=TRUE)
 }
 \references{
 Sonawane, Abhijeet Rajendra, et al. "Constructing gene regulatory networks using epigenetic data." npj Systems Biology and Applications 7.1 (2021): 1-13.