diff --git a/R/SQUIC.R b/R/SQUIC.R
index c7389351be6f805633aa9fd4dc38d16d6c20d2ae..461578d8b38cd2c809b1cc60f008fde5656d4d6b 100644
--- a/R/SQUIC.R
+++ b/R/SQUIC.R
@@ -18,7 +18,7 @@
 usethis::use_package("Matrix") 
 
 # Main function
-SQUIC <- function(Y, lambda, max_iter=100, tol=1e-4,tol_inv=1e-3,verbose=1, M=NULL, X0=NULL, W0=NULL) {
+SQUIC <- function(Y, lambda, max_iter=100, tol=1e-3,tol_inv=1e-4,verbose=1, M=NULL, X0=NULL, W0=NULL) {
   
   verbose <- min(verbose,1)
   
@@ -45,7 +45,6 @@ SQUIC <- function(Y, lambda, max_iter=100, tol=1e-4,tol_inv=1e-3,verbose=1, M=NU
 	  stop('#SQUIC: tol_inv must be great than zero.')
   }
 
-
   if(is.null(M)){
 	  # Make empty sparse matrix of type dgCMatrix.
 	  M <- as(Matrix::sparseMatrix(dims = c(p,p), i={}, j={}),"dgCMatrix")
diff --git a/README.md b/README.md
index afd42ec2307667030e8f375fc60d99550af354af..14be4e1a36ecfd65faa747f2af615bc63840012a 100644
--- a/README.md
+++ b/README.md
@@ -22,22 +22,23 @@ _Note: The environment variable ``SQUIC_LIB_PATH`` defines the location of ``lib
 In this example, we will use SQUIC to estimate the precision matrix of a synthetically generated dataset with correlated random variables, where the true precision matrix is tridiagonal.
 
 ```angular2
-library(SQUIC) 
+library(SQUIC)
 
 p <- 1024
 n <- 100
 lambda <- .4
 
 # generate a tridiagonal matrix
-iC_star <- Matrix::bandSparse(p,p,(-1):1,list(rep(-.5, p-1),rep(1.25,p), rep(-.5,p-1)))
+iC_star <- Matrix::bandSparse(p, p, (-1):1, list(rep(-.5, p-1), rep(1.25,   p), rep(-.5, p-1)))
 
 # generate the data
+set.seed(1)
 z    <- replicate(n,rnorm(p))
 iC_L <- chol(iC_star)
-data <- matrix(solve(iC_L,z),p,n)
+Y    <- matrix(solve(iC_L,z),p,n)
 
-# Run SQUIC
-out <- SQUIC(data,lambda)
+# run SQUIC
+out <- SQUIC(Y,lambda)
 ```
 For further details type ``help(SQUIC)``.
 
diff --git a/man/SQUIC.Rd b/man/SQUIC.Rd
index 0433d2491533a699cadbc4863dba1968af4940a5..7962f05a20659acf5051c17a3cd7d64642ba6961 100644
--- a/man/SQUIC.Rd
+++ b/man/SQUIC.Rd
@@ -11,7 +11,7 @@ large-scale sparse precision matrix estimation.
 }
 
 \usage{
- SQUIC(Y,lambda,max_iter=100,tol=1e-4,tol_inv=1e-3,verbose=1,M=NULL,X0=NULL,W0=NULL)
+ SQUIC(Y,lambda,max_iter=100,tol=1e-3,tol_inv=1e-4,verbose=1,M=NULL,X0=NULL,W0=NULL)
 }
  
 \arguments{
@@ -81,18 +81,19 @@ library(SQUIC)
 
 p <- 1024
 n <- 100
-lambda <- 0.4
+lambda <- .4
 
 # generate a tridiagonal matrix
-iC_star <- Matrix::bandSparse(p,p,(-1):1,list(rep(-.5,p-1),rep(1.25,p),rep(-.5,p-1)))
+iC_star <- Matrix::bandSparse(p, p, (-1):1, list(rep(-.5, p-1), rep(1.25,   p), rep(-.5, p-1)))
 
 # generate the data
-z    <- replicate(n,rnorm(p));
-iC_L <- chol(iC_star);
-data <- matrix(solve(iC_L,z),p,n);
+set.seed(1)
+z    <- replicate(n,rnorm(p))
+iC_L <- chol(iC_star)
+Y    <- matrix(solve(iC_L,z),p,n)
 
-# Run SQUIC
-out <- SQUIC(data,lambda)
+# run SQUIC
+out <- SQUIC(Y,lambda)
 }
 
 \keyword{sparse precision matrix estimation}