diff --git a/DESCRIPTION b/DESCRIPTION
index 06d1572d8a3fc9d704ff11723da2b418fc5005da..e08c3cfb03d1fe70a2dff30420ac2a99966755cc 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,8 +2,8 @@ Package: kfino
 Title: Kalman Filter for Impulse Noised Outliers 
 Version: 1.0.0
 Authors@R: c(
-  person("Bertrand", "Cloez", email = "bertrand.cloez@inrae.fr", role = c("aut", "cre")),
-  person("Isabelle", "Sanchez", email = "isabelle.sanchez@inrae.fr", role = c("ctr")),
+  person("Bertrand", "Cloez", email = "bertrand.cloez@inrae.fr", role = c("aut")),
+  person("Isabelle", "Sanchez", email = "isabelle.sanchez@inrae.fr", role = c("aut", "cre")),
   person("Benedicte", "Fontez", email = "benedicte.fontez@supagro.fr", role = c("ctr")))
 Author: Bertrand Cloez [aut, cre],
   Isabelle Sanchez [ctr],
@@ -20,14 +20,14 @@ BugReports: https://forgemia.inra.fr/isabelle.sanchez/kfino/issues
 Imports: 
     ggplot2,
     dplyr,
-    foreach,
-    doParallel,
-    parallel
 Suggests: 
     rmarkdown,
     knitr,
     testthat (>= 3.0.0),
-    covr
+    covr,
+    foreach,
+    doParallel,
+    parallel
 VignetteBuilder: knitr
 RoxygenNote: 7.2.1
 Config/testthat/edition: 3
diff --git a/R/data.R b/R/data.R
index 5c486ef5c9f97bbcdef2b2747a6eddd5012891ea..792b8275aed4611c52d08d3c9a759f073265a1bb 100644
--- a/R/data.R
+++ b/R/data.R
@@ -1,4 +1,5 @@
-#' a dataset containing the WoW weighing for one animal of 203 observations
+#' a dataset containing the WoW weighing for one animal of 203 observations.
+#' https://doi.org/10.1016/j.compag.2018.08.022
 #'
 #' A dataset for kfino algorithm
 #' @format a data.frame
@@ -13,7 +14,8 @@
 #' }
 "spring1"
 
-#' a dataset containing the WoW weighing for one animal (merinos lamb) of 397 observations
+#' a dataset containing the WoW weighing for one animal (merinos lamb) of 397 
+#' observations. https://doi.org/10.1016/j.compag.2018.08.022
 #'
 #' A dataset for kfino algorithm
 #' @format a data.frame
@@ -29,7 +31,7 @@
 "merinos1"
 
 #' a dataset containing the WoW weighing for one animal (merinos lamb) of 345
-#' observations, difficult to model
+#' observations, difficult to model. https://doi.org/10.1016/j.compag.2018.08.022
 #'
 #' A dataset for kfino algorithm
 #' @format a data.frame
@@ -44,7 +46,8 @@
 #' }
 "merinos2"
 
-#' a dataset containing the WoW weighing for 4 animals of 1296 observations
+#' a dataset containing the WoW weighing for 4 animals of 1296 observations,
+#' https://doi.org/10.1016/j.compag.2018.08.022
 #'
 #' A dataset for kfino algorithm
 #' @format a data.frame
diff --git a/R/graph_functions.R b/R/graph_functions.R
index 2f4678386a82a28b3f6987cbed3d8b312b9b471f..f8bf28a08d033e023b4a85f9f91f81b5e081ba91 100644
--- a/R/graph_functions.R
+++ b/R/graph_functions.R
@@ -13,11 +13,11 @@
 #'
 #' @details The produced graphic can be, according to typeG:
 #' \describe{
-#'  \item{quali}{The detection of outliers with a qualitative rule: OK values,
-#'       KO values (outliers) and OOR values (out of range values defined 
-#'       by the user in `kfino_fit`) }
-#'  \item{quanti}{The detection of outliers with a quantitative display using
-#'       the calculated probability of the kfino algorithm}
+#'  \item{quali}{This plot shows the detection of outliers with a qualitative 
+#'       rule: OK values (black), KO values (outliers, purple) and OOR values 
+#'       (out of range values defined by the user in `kfino_fit`, red) }
+#'  \item{quanti}{This plot shows the detection of outliers with a quantitative 
+#'       display using the calculated probability of the kfino algorithm}
 #'  \item{prediction}{This plot shows the prediction of the analyzed variable 
 #'        plus the OK values. Prediction corresponds to E[X_{t} | Y_{1...t}] 
 #'        for each time point t. Between 2 time points, we used a simple 
diff --git a/R/kfino.R b/R/kfino.R
index 9fce17e8ce86e35ce30ea4094ca49d0e2c74ce0c..2ee4611377fb7061960c2d931d308ee0e85a3011 100644
--- a/R/kfino.R
+++ b/R/kfino.R
@@ -15,6 +15,7 @@
 #'        default 10
 #' @param kappaOpt numeric, truncation setting for initial parameters' 
 #'        optimization, default 7
+#' @param verbose write stuff if TRUE (optional), default FALSE.
 #'
 #' @details The initialization parameter list `param` contains:
 #' \describe{
@@ -37,8 +38,8 @@
 #'  \item{seqp}{numeric vector, sequence of pp probability to be correctly 
 #'              weighted. default seq(0.5,0.7,0.1)}
 #' }
-#' It has to be given by the user following his knowledge of the animal or
-#' the data set. All parameters are compulsory except m0, mm and pp that can be
+#' It should be given by the user based on their knowledge of the animal or the 
+#' data set. All parameters are compulsory except m0, mm and pp that can be
 #' optimized by the algorithm. In the optimization step, those three parameters
 #' are initialized according to the input data (between the expert
 #' range) using quantile of the Y distribution (varying between 0.2 and 0.8 for
@@ -53,22 +54,27 @@
 #'
 #' @return a S3 list with two data frames and a list of vectors of
 #' kfino results
-#' \describe{
-#' \item{detectOutlier}{The whole input data set with the detected outliers 
-#'                      flagged and prediction}
+#' 
+#' @return detectOutlier: The whole input data set with the detected outliers 
+#'                      flagged and the prediction of the analyzed variable. 
+#'                      the following columns are joined to the columns 
+#'                      present in the input data set:
 #'  \describe{
 #'   \item{prediction}{the parameter of interest - Yvar - predicted}
 #'   \item{label_pred}{the probability of the value being well predicted}
 #'   \item{lwr}{lower bound of the confidence interval of the predicted value}
-#'   \item{upper}{upper bound of the confidence interval of the predicted value}
+#'   \item{upr}{upper bound of the confidence interval of the predicted value}
 #'   \item{flag}{flag of the value (OK value, KO value (outlier), OOR value
 #'               (out of range values defined by the user in `kfino_fit`)}
 #'  }
-#' \item{PredictionOK}{A dataset with the predictions on possible values (OK 
-#'                     and KO values)}
-#' \item{kfino.results}{kfino results (a list of vectors) on optimized input
-#'                      parameters or not}
-#' }
+#' @return PredictionOK: A subset of `detectOutlier` data set with the predictions 
+#'         of the analyzed variable on possible values (OK and KO values)
+#' @return kfino.results: kfino results (a list of vectors containing the 
+#'         prediction of the analyzed variable, the probability to be an 
+#'         outlier, the likelihood, the confidence interval of 
+#'         the prediction and the flag of the data) on input parameters that 
+#'         were optimized if the user chose this option
+#'
 #' @export
 #' @examples
 #' data(spring1)
@@ -90,14 +96,16 @@
 #'
 #' resu1<-kfino_fit(datain=spring1,
 #'               Tvar="dateNum",Yvar="Poids",
-#'               doOptim=TRUE,method="ML",param=param1)
+#'               doOptim=TRUE,method="ML",param=param1,
+#'               verbose=TRUE)
 #' Sys.time() - t0
 #'
 #' # --- With Optimization on initial parameters - EM method
 #' t0 <- Sys.time()
 #' resu1b<-kfino_fit(datain=spring1,
 #'               Tvar="dateNum",Yvar="Poids",
-#'               doOptim=TRUE,method="EM",param=param1)
+#'               doOptim=TRUE,method="EM",param=param1,
+#'               verbose=TRUE)
 #' Sys.time() - t0
 #'
 #' # --- Without Optimization on initial parameters
@@ -116,7 +124,8 @@
 #' resu2<-kfino_fit(datain=spring1,
 #'               Tvar="dateNum",Yvar="Poids",
 #'               param=param2,
-#'               doOptim=FALSE)
+#'               doOptim=FALSE,
+#'               verbose=FALSE)
 #' Sys.time() - t0
 #'
 #' # complex data on merinos2 dataset
@@ -141,7 +150,8 @@
 kfino_fit<-function(datain,Tvar,Yvar,
                     param=NULL,
                     doOptim=TRUE,method="ML",
-                    threshold=0.5,kappa=10,kappaOpt=7){
+                    threshold=0.5,kappa=10,kappaOpt=7,
+                    verbose=FALSE){
 
   if( any(is.null(param[["expertMin"]]) |
           is.null(param[["expertMax"]])) )
@@ -206,16 +216,20 @@ kfino_fit<-function(datain,Tvar,Yvar,
 
     if (N > 500){
       # optim with sub-sampling
-      print("-------:")
-      print("Optimization of initial parameters ")
-      print("with sub-sampling and ML method - result:")
+      if (verbose){
+        print("-------:")
+        print("Optimization of initial parameters ")
+        print("with sub-sampling and ML method - result:")
+      }
       bornem0=quantile(Y[1:N/4], probs = c(.2, .8))
       m0opt=quantile(Y[1:N/4], probs = c(.5))
       mmopt=quantile(Y[(3*N/4):N], probs = c(.5))
 
-      cat("range m0: ",bornem0,"\n")
-      cat("Initial m0opt: ",m0opt,"\n")
-      cat("Initial mmopt: ",mmopt,"\n")
+      if (verbose){
+        cat("range m0: ",bornem0,"\n")
+        cat("Initial m0opt: ",m0opt,"\n")
+        cat("Initial mmopt: ",mmopt,"\n")
+      }
       popt=0.5
       #--- Saving datain before sub-sampling
       YY=Y
@@ -274,11 +288,13 @@ kfino_fit<-function(datain,Tvar,Yvar,
       Y=YY
       Tps=TpsTps
       N=NN
-      print("Optimized parameters with ML method: ")
-      cat("Optimized m0: ",m0opt,"\n")
-      cat("Optimized mm: ",mmopt,"\n")
-      cat("Optimized pp: ",popt,"\n")
-      print("-------:")
+      if (verbose){
+        print("Optimized parameters with ML method: ")
+        cat("Optimized m0: ",m0opt,"\n")
+        cat("Optimized mm: ",mmopt,"\n")
+        cat("Optimized pp: ",popt,"\n")
+        print("-------:")
+      }
 
       resultat=KBO_known(param=list(mm=mmopt,
                                     pp=popt,
@@ -295,11 +311,13 @@ kfino_fit<-function(datain,Tvar,Yvar,
     } else if (N > 50){
       # optimization without sub-sampling, 2 methods, EM or ML
       if (method == "EM"){
+        if (verbose){
           print("-------:")
           print("Optimization of initial parameters with EM method - result:")
           print("no sub-sampling performed:")
-          bornem0=quantile(Y[1:N/2], probs = c(.2, .8))
-          cat("range m0: ",bornem0,"\n")
+        }
+        bornem0=quantile(Y[1:N/2], probs = c(.2, .8))
+        if (verbose)  cat("range m0: ",bornem0,"\n")
 
           #--- par dichotomie
           # borne basse
@@ -448,11 +466,13 @@ kfino_fit<-function(datain,Tvar,Yvar,
             popt<-popt_low
           }
           
-          print("Optimized parameters with EM method: ")
-          cat("Optimized m0: ",m0opt,"\n")
-          cat("Optimized mm: ",mmopt,"\n")
-          cat("Optimized pp: ",popt,"\n")
-          print("-------:")
+          if (verbose){
+            print("Optimized parameters with EM method: ")
+            cat("Optimized m0: ",m0opt,"\n")
+            cat("Optimized mm: ",mmopt,"\n")
+            cat("Optimized pp: ",popt,"\n")
+            print("-------:")
+          }
           resultat=KBO_known(param=list(mm=mmopt,
                                         pp=popt,
                                         m0=m0opt,
@@ -466,17 +486,20 @@ kfino_fit<-function(datain,Tvar,Yvar,
                              threshold=threshold,Y=Y,Tps=Tps,N=N,kappa=kappa)
           
       } else if (method == "ML"){
-        print("-------:")
-        print("Optimization of initial parameters with ML method - result:")
-        print("no sub-sampling performed:")
+        if (verbose){
+          print("-------:")
+          print("Optimization of initial parameters with ML method - result:")
+          print("no sub-sampling performed:")
+        }
         bornem0=quantile(Y[1:N/4], probs = c(.2, .8))
         m0opt=quantile(Y[1:N/4], probs = c(.5))
         mmopt=quantile(Y[(3*N/4):N], probs = c(.5))
 
-        cat("range m0: ",bornem0,"\n")
-        cat("initial m0opt: ",m0opt,"\n")
-        cat("initial mmopt: ",mmopt,"\n")
-
+        if (verbose){
+          cat("range m0: ",bornem0,"\n")
+          cat("initial m0opt: ",m0opt,"\n")
+          cat("initial mmopt: ",mmopt,"\n")
+        }
         popt=0.5
 
         Vopt=KBO_L(list(m0=m0opt,
@@ -515,11 +538,13 @@ kfino_fit<-function(datain,Tvar,Yvar,
           }
         }
 
-        print("Optimized parameters: ")
-        cat("Optimized m0: ",m0opt,"\n")
-        cat("Optimized mm: ",mmopt,"\n")
-        cat("Optimized pp: ",popt,"\n")
-        print("-------:")
+        if (verbose){
+          print("Optimized parameters: ")
+          cat("Optimized m0: ",m0opt,"\n")
+          cat("Optimized mm: ",mmopt,"\n")
+          cat("Optimized pp: ",popt,"\n")
+          print("-------:")
+        }
         resultat=KBO_known(param=list(mm=mmopt,
                                       pp=popt,
                                       m0=m0opt,
@@ -542,12 +567,15 @@ kfino_fit<-function(datain,Tvar,Yvar,
         } else {
           X<-c(m0,pp,mm)
         }
-        print("-------:")
-        print("Optimization of initial parameters - result:")
-        print("Not enough data => No optimization performed:")
-        print("Used parameters: ")
-        print(X)
-        print("-------:")
+        
+        if (verbose){
+          print("-------:")
+          print("Optimization of initial parameters - result:")
+          print("Not enough data => No optimization performed:")
+          print("Used parameters: ")
+          print(X)
+          print("-------:")
+        }
         resultat=KBO_known(param=list(m0=X[[1]],
                                       pp=X[[2]],
                                       mm=X[[3]],
@@ -574,10 +602,12 @@ kfino_fit<-function(datain,Tvar,Yvar,
       } else {
         X<-c(m0,pp,mm)
       }
-      print("-------:")
-      print("No optimization of initial parameters:")
-      print("Used parameters: ")
-      print(X)
+      if (verbose){
+        print("-------:")
+        print("No optimization of initial parameters:")
+        print("Used parameters: ")
+        print(X)
+      }
       resultat=KBO_known(param=list(m0=X[[1]],
                                     pp=X[[2]],
                                     mm=X[[3]],
diff --git a/README.md b/README.md
index 81b24e0c4e2c57316054d7c057ab796bd55fa067..04277cef60f7a90fa1b40ffbb23653ffe8a130bc 100644
--- a/README.md
+++ b/README.md
@@ -2,13 +2,13 @@
 
 # kfino <img src='man/figures/logo.png' align="right" height="139" />
 
-Kalman Filter for Impulse Noised Outliers
+The **kfino** algorithm was developped for time courses in order to detect impulse noised outliers and predict the parameter of interest mainly for data recorded on the walk-over-weighing system described in this publication:
 
-OBJECTIVE AND DESCRIPTION ALGO
+E.González-García *et. al.* (2018) A mobile and automated walk-over-weighing system for a close and remote monitoring of liveweight in sheep. vol 153: 226-238. https://doi.org/10.1016/j.compag.2018.08.022
 
-CREER SITE PKGDOWN
+**Kalman filter with impulse noised outliers** (kfino) is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations.
 
-**in progress**
+The method is described in full details in the following arxiv preprint: https://arxiv.org/abs/2208.00961.
 
 ## Installation
 
@@ -30,10 +30,23 @@ library(kfino)
 help(package="kfino")
 ```
 
+Please, have a look to the vignettes that explain how to use the algorithm. The 
+main specifications are:
+
+* filtering data with a large number of outliers 
+* predicting the analyzed variable
+* providing useful graphics to interpret the data
+
+![quali](man/figures/kfino_plot_quali.png)
+
+![quanti](man/figures/kfino_plot_quanti.png)
+
+![pred](man/figures/kfino_plot_pred.png)
+
 ## Citation
 As a lot of time and effort were spent in creating the kfino algorithm, please cite it when using it for data analysis:
 
-XXX
+https://arxiv.org/abs/2208.00961.
 
 See also citation() for citing R itself.
 
diff --git a/doc/HowTo.R b/doc/HowTo.R
index bd5c3d63aecc2538309537371074bf524e7dc5a0..bff8f505abc8ee417d5da92e754b0bdfbe68fb54 100644
--- a/doc/HowTo.R
+++ b/doc/HowTo.R
@@ -34,8 +34,20 @@ param2<-list(m0=41,
 resu2<-kfino_fit(datain=spring1,
               Tvar="dateNum",Yvar="Poids",
               param=param2,
-              doOptim=FALSE)     
+              doOptim=FALSE,
+              verbose=TRUE)     
 
+## -----------------------------------------------------------------------------
+# structure of detectOutlier data set
+str(resu2$detectOutlier)
+
+# head of PredictionOK data set
+head(resu2$PredictionOK)
+
+# structure of kfino.results list
+str(resu2$kfino.results)
+
+## -----------------------------------------------------------------------------
 # flags are qualitative
 kfino_plot(resuin=resu2,typeG="quali",
             Tvar="Day",Yvar="Poids",Ident="IDE")
@@ -60,7 +72,9 @@ param1<-list(m0=NULL,
 
 resu1<-kfino_fit(datain=spring1,
               Tvar="dateNum",Yvar="Poids",
-              doOptim=TRUE,param=param1)  
+              param=param1,
+              doOptim=TRUE,
+              verbose=TRUE)  
 
 # flags are qualitative
 kfino_plot(resuin=resu1,typeG="quali",
@@ -99,7 +113,8 @@ param3<-list(m0=NULL,
 
 resu3<-kfino_fit(datain=merinos1,
               Tvar="dateNum",Yvar="Poids",
-              doOptim=TRUE,param=param3)      
+              doOptim=TRUE,param=param3,
+              verbose=TRUE)      
 
 # flags are qualitative
 kfino_plot(resuin=resu3,typeG="quali",
diff --git a/doc/HowTo.Rmd b/doc/HowTo.Rmd
index 882ec6c80237d52ec6057db903e6bcb7dbbb4b1b..f03e8ac0bd5b369f6dd7acc2298715522d7398e7 100644
--- a/doc/HowTo.Rmd
+++ b/doc/HowTo.Rmd
@@ -58,9 +58,9 @@ dim(spring1)
 head(spring1)
 ```
 
-The range weight of this animal is between 30 and 75 kg and must be given in the initial parameters of the `kfino_fit()`function.
+The range weight of this animal is between 30 and 75 kg and must be given in `param`, a list of initial parameters to include in the `kfino_fit()` function call.
 
-The user can either perform an outlier detection (and prediction) given initial parameters or on optimized initial parameters (on m0, mm and pp):
+The user can either perform an outlier detection (and prediction) given initial parameters or on optimized initial parameters (on m0, mm and pp). `param` list is composed of:
 
 * m0 = (optional) the initial weight, NULL if the user wants to optimize it,
 * mm = (optional) the target weight, NULL if the user wants to optimize it,
@@ -77,7 +77,7 @@ The user can either perform an outlier detection (and prediction) given initial
 # Kfino algorithm on the `spring1` dataset
 ## Parameters (m0, mm and pp) not optimized
 
-If the user chooses to not optimize the initial parameters, all the list must be completed according to expert knowledge of the data set.
+If the user chooses to not optimize the initial parameters, all the list must be completed according to expert knowledge of the data set. Here, the user supposes that the initial weight is around 41 and the target one around 45.
 
 ```{r,error=TRUE}
 # --- Without Optimisation on parameters
@@ -96,8 +96,39 @@ param2<-list(m0=41,
 resu2<-kfino_fit(datain=spring1,
               Tvar="dateNum",Yvar="Poids",
               param=param2,
-              doOptim=FALSE)     
+              doOptim=FALSE,
+              verbose=TRUE)     
+```
+
+resu2 is a list of 3 elements:
+
+* detectOutlier: The whole input data set with the detected outliers flagged and the prediction of the analyzed variable. the following columns are joined to the columns present in the input data set:
+
+    - prediction: the parameter of interest - Yvar - predicted
+    - label_pred: the probability of the value being well predicted
+    - lwr: lower bound of the confidence interval of the predicted value
+    - upr: upper bound of the confidence interval of the predicted value
+    - flag: flag of the value (OK value, KO value (outlier), OOR value
+        (out of range values defined by the user in `kfino_fit`)
+
+* PredictionOK: A subset of `detectOutlier` data set with the predictions 
+        of the analyzed variable on possible values (OK and KO values)
+* kfino.results: kfino results (a list of vectors, prediction, probability to be an outlier , likelihood, confidence interval of the prediction and the flag of the data) on input parameters that were optimized if the user chooses this option
+
+```{r}
+# structure of detectOutlier data set
+str(resu2$detectOutlier)
 
+# head of PredictionOK data set
+head(resu2$PredictionOK)
+
+# structure of kfino.results list
+str(resu2$kfino.results)
+```
+
+Using the `kfino_plot()`function allows the user to visualize the results:
+
+```{r}
 # flags are qualitative
 kfino_plot(resuin=resu2,typeG="quali",
             Tvar="Day",Yvar="Poids",Ident="IDE")
@@ -107,6 +138,8 @@ kfino_plot(resuin=resu2,typeG="quanti",
             Tvar="Day",Yvar="Poids",Ident="IDE")
 ```
 
+
+
 ## Parameters (m0, mm and pp) optimized
 
 If the user chooses to optimize the initial parameters, m0, mm and pp must be set to NULL.
@@ -127,7 +160,9 @@ param1<-list(m0=NULL,
 
 resu1<-kfino_fit(datain=spring1,
               Tvar="dateNum",Yvar="Poids",
-              doOptim=TRUE,param=param1)  
+              param=param1,
+              doOptim=TRUE,
+              verbose=TRUE)  
 
 # flags are qualitative
 kfino_plot(resuin=resu1,typeG="quali",
@@ -178,7 +213,8 @@ param3<-list(m0=NULL,
 
 resu3<-kfino_fit(datain=merinos1,
               Tvar="dateNum",Yvar="Poids",
-              doOptim=TRUE,param=param3)      
+              doOptim=TRUE,param=param3,
+              verbose=TRUE)      
 
 # flags are qualitative
 kfino_plot(resuin=resu3,typeG="quali",
diff --git a/doc/HowTo.html b/doc/HowTo.html
index 9df2d0367831b0b2266b71d1e959698e73e10f9c..fca9e4f2dbaed28b53415c66afdad0cc687ddce7 100644
--- a/doc/HowTo.html
+++ b/doc/HowTo.html
@@ -1583,11 +1583,11 @@ head(spring1)
 #&gt; 5  23.4 2017-05-25 00:00:00 250016286863027 2017-05-25 05:58:00   1.03 
 #&gt; 6   0   2017-05-25 00:00:00 250016286863027 2017-05-25 09:30:00   1.17</code></pre>
 <p>The range weight of this animal is between 30 and 75 kg and must be
-given in the initial parameters of the
-<code>kfino_fit()</code>function.</p>
+given in <code>param</code>, a list of initial parameters to include in
+the <code>kfino_fit()</code> function call.</p>
 <p>The user can either perform an outlier detection (and prediction)
 given initial parameters or on optimized initial parameters (on m0, mm
-and pp):</p>
+and pp). <code>param</code> list is composed of:</p>
 <ul>
 <li>m0 = (optional) the initial weight, NULL if the user wants to
 optimize it,</li>
@@ -1616,8 +1616,9 @@ weighted. default seq(0.5,0.7,0.1)</li>
 <h2><span class="header-section-number">2.1</span> Parameters (m0, mm
 and pp) not optimized</h2>
 <p>If the user chooses to not optimize the initial parameters, all the
-list must be completed according to expert knowledge of the data
-set.</p>
+list must be completed according to expert knowledge of the data set.
+Here, the user supposes that the initial weight is around 41 and the
+target one around 45.</p>
 <pre class="r"><code># --- Without Optimisation on parameters
 param2&lt;-list(m0=41,
              mm=45,
@@ -1634,13 +1635,73 @@ param2&lt;-list(m0=41,
 resu2&lt;-kfino_fit(datain=spring1,
               Tvar=&quot;dateNum&quot;,Yvar=&quot;Poids&quot;,
               param=param2,
-              doOptim=FALSE)     
+              doOptim=FALSE,
+              verbose=TRUE)     
 #&gt; [1] &quot;-------:&quot;
 #&gt; [1] &quot;No optimization of initial parameters:&quot;
 #&gt; [1] &quot;Used parameters: &quot;
-#&gt; [1] 41.0  0.5 45.0
-
-# flags are qualitative
+#&gt; [1] 41.0  0.5 45.0</code></pre>
+<p>resu2 is a list of 3 elements:</p>
+<ul>
+<li><p>detectOutlier: The whole input data set with the detected
+outliers flagged and the prediction of the analyzed variable. the
+following columns are joined to the columns present in the input data
+set:</p>
+<ul>
+<li>prediction: the parameter of interest - Yvar - predicted</li>
+<li>label_pred: the probability of the value being well predicted</li>
+<li>lwr: lower bound of the confidence interval of the predicted
+value</li>
+<li>upr: upper bound of the confidence interval of the predicted
+value</li>
+<li>flag: flag of the value (OK value, KO value (outlier), OOR value
+(out of range values defined by the user in <code>kfino_fit</code>)</li>
+</ul></li>
+<li><p>PredictionOK: A subset of <code>detectOutlier</code> data set
+with the predictions of the analyzed variable on possible values (OK and
+KO values)</p></li>
+<li><p>kfino.results: kfino results (a list of vectors, prediction,
+probability to be an outlier , likelihood, confidence interval of the
+prediction and the flag of the data) on input parameters that were
+optimized if the user chooses this option</p></li>
+</ul>
+<pre class="r"><code># structure of detectOutlier data set
+str(resu2$detectOutlier)
+#&gt; &#39;data.frame&#39;:    203 obs. of  11 variables:
+#&gt;  $ Poids     : num  28.6 45 25 43 23.4 0 42.2 43 85.4 40.1 ...
+#&gt;  $ Date      : POSIXct, format: &quot;2017-05-24&quot; &quot;2017-05-24&quot; ...
+#&gt;  $ IDE       : chr  &quot;250016286863027&quot; &quot;250016286863027&quot; &quot;250016286863027&quot; &quot;250016286863027&quot; ...
+#&gt;  $ Day       : POSIXct, format: &quot;2017-05-24 16:34:00&quot; &quot;2017-05-24 19:24:00&quot; ...
+#&gt;  $ dateNum   : num  0.469 0.587 1.004 1.018 1.027 ...
+#&gt;  $ rowNum    : int  1 2 3 4 5 6 7 8 9 10 ...
+#&gt;  $ prediction: num  NA 41.5 NA 41.7 NA ...
+#&gt;  $ label_pred: num  NA 0.68 NA 0.88 NA NA 0.9 0.88 NA 0.87 ...
+#&gt;  $ lwr       : num  NA 39.5 NA 39.9 NA ...
+#&gt;  $ upr       : num  NA 43.4 NA 43.5 NA ...
+#&gt;  $ flag      : chr  &quot;OOR&quot; &quot;OK&quot; &quot;OOR&quot; &quot;OK&quot; ...
+
+# head of PredictionOK data set
+head(resu2$PredictionOK)
+#&gt;   rowNum prediction label_pred      lwr      upr flag
+#&gt; 1      2   41.45659       0.68 39.49659 43.41659   OK
+#&gt; 2      4   41.68643       0.88 39.88262 43.49024   OK
+#&gt; 3      7   41.75829       0.90 40.07535 43.44123   OK
+#&gt; 4      8   41.90155       0.88 40.32265 43.48044   OK
+#&gt; 5     10   41.71243       0.87 40.14772 43.27714   OK
+#&gt; 6     11   41.81293       0.89 40.33186 43.29400   OK
+
+# structure of kfino.results list
+str(resu2$kfino.results)
+#&gt; List of 6
+#&gt;  $ prediction: num [1:121] 41.5 41.7 41.8 41.9 41.7 ...
+#&gt;  $ label     : num [1:121] 0.685 0.875 0.895 0.884 0.874 ...
+#&gt;  $ likelihood: num [1, 1] 1.25e-150
+#&gt;  $ lwr       : num [1:121] 39.5 39.9 40.1 40.3 40.1 ...
+#&gt;  $ upr       : num [1:121] 43.4 43.5 43.4 43.5 43.3 ...
+#&gt;  $ flag      : chr [1:121] &quot;OK&quot; &quot;OK&quot; &quot;OK&quot; &quot;OK&quot; ...</code></pre>
+<p>Using the <code>kfino_plot()</code>function allows the user to
+visualize the results:</p>
+<pre class="r"><code># flags are qualitative
 kfino_plot(resuin=resu2,typeG=&quot;quali&quot;,
             Tvar=&quot;Day&quot;,Yvar=&quot;Poids&quot;,Ident=&quot;IDE&quot;)</code></pre>
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" width="672" /></p>
@@ -1670,7 +1731,9 @@ param1&lt;-list(m0=NULL,
 
 resu1&lt;-kfino_fit(datain=spring1,
               Tvar=&quot;dateNum&quot;,Yvar=&quot;Poids&quot;,
-              doOptim=TRUE,param=param1)  
+              param=param1,
+              doOptim=TRUE,
+              verbose=TRUE)  
 #&gt; [1] &quot;-------:&quot;
 #&gt; [1] &quot;Optimization of initial parameters with ML method - result:&quot;
 #&gt; [1] &quot;no sub-sampling performed:&quot;
@@ -1748,7 +1811,8 @@ param3&lt;-list(m0=NULL,
 
 resu3&lt;-kfino_fit(datain=merinos1,
               Tvar=&quot;dateNum&quot;,Yvar=&quot;Poids&quot;,
-              doOptim=TRUE,param=param3)      
+              doOptim=TRUE,param=param3,
+              verbose=TRUE)      
 #&gt; [1] &quot;-------:&quot;
 #&gt; [1] &quot;Optimization of initial parameters with ML method - result:&quot;
 #&gt; [1] &quot;no sub-sampling performed:&quot;
@@ -1810,13 +1874,13 @@ informations</h1>
 #&gt; [1] stats     graphics  grDevices utils     datasets  methods   base     
 #&gt; 
 #&gt; other attached packages:
-#&gt; [1] ggplot2_3.3.6 dplyr_1.0.9   kfino_1.0.0  
+#&gt; [1] ggplot2_3.3.6 dplyr_1.0.10  kfino_1.0.0  
 #&gt; 
 #&gt; loaded via a namespace (and not attached):
 #&gt;  [1] highr_0.9        pillar_1.8.1     bslib_0.4.0      compiler_4.2.1  
 #&gt;  [5] jquerylib_0.1.4  tools_4.2.1      digest_0.6.29    jsonlite_1.8.0  
-#&gt;  [9] evaluate_0.16    lifecycle_1.0.1  tibble_3.1.8     gtable_0.3.0    
-#&gt; [13] pkgconfig_2.0.3  rlang_1.0.4      cli_3.3.0        DBI_1.1.3       
+#&gt;  [9] evaluate_0.16    lifecycle_1.0.1  tibble_3.1.8     gtable_0.3.1    
+#&gt; [13] pkgconfig_2.0.3  rlang_1.0.5      cli_3.3.0        DBI_1.1.3       
 #&gt; [17] rstudioapi_0.14  yaml_2.3.5       xfun_0.32        fastmap_1.1.0   
 #&gt; [21] withr_2.5.0      stringr_1.4.1    knitr_1.40       generics_0.1.3  
 #&gt; [25] vctrs_0.4.1      sass_0.4.2       grid_4.2.1       tidyselect_1.1.2
@@ -1824,7 +1888,7 @@ informations</h1>
 #&gt; [33] farver_2.1.1     purrr_0.3.4      magrittr_2.0.3   ellipsis_0.3.2  
 #&gt; [37] scales_1.2.1     htmltools_0.5.3  assertthat_0.2.1 colorspace_2.0-3
 #&gt; [41] labeling_0.4.2   utf8_1.2.2       stringi_1.7.8    munsell_0.5.0   
-#&gt; [45] cachem_1.0.6     crayon_1.5.1</code></pre>
+#&gt; [45] cachem_1.0.6</code></pre>
 </div>
 
 
diff --git a/man/figures/kfino_plot_pred.png b/man/figures/kfino_plot_pred.png
new file mode 100644
index 0000000000000000000000000000000000000000..7e354ff6f55408ce613d1957f06a266a9fb8ed6f
Binary files /dev/null and b/man/figures/kfino_plot_pred.png differ
diff --git a/man/figures/kfino_plot_quali.png b/man/figures/kfino_plot_quali.png
new file mode 100644
index 0000000000000000000000000000000000000000..d702618da762af4e2ee01c46dd89d97da8c6479f
Binary files /dev/null and b/man/figures/kfino_plot_quali.png differ
diff --git a/man/figures/kfino_plot_quanti.png b/man/figures/kfino_plot_quanti.png
new file mode 100644
index 0000000000000000000000000000000000000000..5b0a970b4db80f4c4dab90b190175f9d9188eee6
Binary files /dev/null and b/man/figures/kfino_plot_quanti.png differ
diff --git a/man/kfino.Rd b/man/kfino.Rd
index 6d05bfa9527140e2c3bbb8cfd19766c1f66f24b6..19a0444fe19ccd3c4668e44495b10f7875dcd251 100644
--- a/man/kfino.Rd
+++ b/man/kfino.Rd
@@ -22,11 +22,15 @@ Useful links:
 
 }
 \author{
-\strong{Maintainer}: Bertrand Cloez \email{bertrand.cloez@inrae.fr}
+\strong{Maintainer}: Isabelle Sanchez \email{isabelle.sanchez@inrae.fr}
+
+Authors:
+\itemize{
+  \item Bertrand Cloez \email{bertrand.cloez@inrae.fr}
+}
 
 Other contributors:
 \itemize{
-  \item Isabelle Sanchez \email{isabelle.sanchez@inrae.fr} [contractor]
   \item Benedicte Fontez \email{benedicte.fontez@supagro.fr} [contractor]
 }
 
diff --git a/man/kfino_fit.Rd b/man/kfino_fit.Rd
index 5a346be1320b4e4947eeac1c4575a05f38f7e345..7d326d8e20782cde1dd9c8c39705c6a8052404fc 100644
--- a/man/kfino_fit.Rd
+++ b/man/kfino_fit.Rd
@@ -13,7 +13,8 @@ kfino_fit(
   method = "ML",
   threshold = 0.5,
   kappa = 10,
-  kappaOpt = 7
+  kappaOpt = 7,
+  verbose = FALSE
 )
 }
 \arguments{
@@ -41,26 +42,34 @@ default 10}
 
 \item{kappaOpt}{numeric, truncation setting for initial parameters' 
 optimization, default 7}
+
+\item{verbose}{write stuff if TRUE (optional), default FALSE.}
 }
 \value{
 a S3 list with two data frames and a list of vectors of
 kfino results
-\describe{
-\item{detectOutlier}{The whole input data set with the detected outliers 
-                     flagged and prediction}
+
+detectOutlier: The whole input data set with the detected outliers 
+                     flagged and the prediction of the analyzed variable. 
+                     the following columns are joined to the columns 
+                     present in the input data set:
  \describe{
   \item{prediction}{the parameter of interest - Yvar - predicted}
   \item{label_pred}{the probability of the value being well predicted}
   \item{lwr}{lower bound of the confidence interval of the predicted value}
-  \item{upper}{upper bound of the confidence interval of the predicted value}
+  \item{upr}{upper bound of the confidence interval of the predicted value}
   \item{flag}{flag of the value (OK value, KO value (outlier), OOR value
               (out of range values defined by the user in `kfino_fit`)}
  }
-\item{PredictionOK}{A dataset with the predictions on possible values (OK 
-                    and KO values)}
-\item{kfino.results}{kfino results (a list of vectors) on optimized input
-                     parameters or not}
-}
+
+PredictionOK: A subset of `detectOutlier` data set with the predictions 
+        of the analyzed variable on possible values (OK and KO values)
+
+kfino.results: kfino results (a list of vectors containing the 
+        prediction of the analyzed variable, the probability to be an 
+        outlier, the likelihood, the confidence interval of 
+        the prediction and the flag of the data) on input parameters that 
+        were optimized if the user chose this option
 }
 \description{
 kfino_fit a function to detect outlier with a Kalman Filtering approach
@@ -87,8 +96,8 @@ The initialization parameter list `param` contains:
  \item{seqp}{numeric vector, sequence of pp probability to be correctly 
              weighted. default seq(0.5,0.7,0.1)}
 }
-It has to be given by the user following his knowledge of the animal or
-the data set. All parameters are compulsory except m0, mm and pp that can be
+It should be given by the user based on their knowledge of the animal or the 
+data set. All parameters are compulsory except m0, mm and pp that can be
 optimized by the algorithm. In the optimization step, those three parameters
 are initialized according to the input data (between the expert
 range) using quantile of the Y distribution (varying between 0.2 and 0.8 for
@@ -117,14 +126,16 @@ param1<-list(m0=NULL,
 
 resu1<-kfino_fit(datain=spring1,
               Tvar="dateNum",Yvar="Poids",
-              doOptim=TRUE,method="ML",param=param1)
+              doOptim=TRUE,method="ML",param=param1,
+              verbose=TRUE)
 Sys.time() - t0
 
 # --- With Optimization on initial parameters - EM method
 t0 <- Sys.time()
 resu1b<-kfino_fit(datain=spring1,
               Tvar="dateNum",Yvar="Poids",
-              doOptim=TRUE,method="EM",param=param1)
+              doOptim=TRUE,method="EM",param=param1,
+              verbose=TRUE)
 Sys.time() - t0
 
 # --- Without Optimization on initial parameters
@@ -143,7 +154,8 @@ param2<-list(m0=41,
 resu2<-kfino_fit(datain=spring1,
               Tvar="dateNum",Yvar="Poids",
               param=param2,
-              doOptim=FALSE)
+              doOptim=FALSE,
+              verbose=FALSE)
 Sys.time() - t0
 
 # complex data on merinos2 dataset
diff --git a/man/kfino_plot.Rd b/man/kfino_plot.Rd
index 140cd41ee74b0c25f34823c3db324d6160d425bc..4eca98f78e1bddbb0b59756ca31e282bbfe0fe61 100644
--- a/man/kfino_plot.Rd
+++ b/man/kfino_plot.Rd
@@ -43,11 +43,11 @@ kfino_plot a graphical function for the result of a kfino run
 \details{
 The produced graphic can be, according to typeG:
 \describe{
- \item{quali}{The detection of outliers with a qualitative rule: OK values,
-      KO values (outliers) and OOR values (out of range values defined 
-      by the user in `kfino_fit`) }
- \item{quanti}{The detection of outliers with a quantitative display using
-      the calculated probability of the kfino algorithm}
+ \item{quali}{This plot shows the detection of outliers with a qualitative 
+      rule: OK values (black), KO values (outliers, purple) and OOR values 
+      (out of range values defined by the user in `kfino_fit`, red) }
+ \item{quanti}{This plot shows the detection of outliers with a quantitative 
+      display using the calculated probability of the kfino algorithm}
  \item{prediction}{This plot shows the prediction of the analyzed variable 
        plus the OK values. Prediction corresponds to E[X_{t} | Y_{1...t}] 
        for each time point t. Between 2 time points, we used a simple 
diff --git a/man/lambs.Rd b/man/lambs.Rd
index 47c8129c820a6f7683ce34187cb4889e34ee5ba3..00b4c88a08791ec69c4ab17a64887a1d4dd984e7 100644
--- a/man/lambs.Rd
+++ b/man/lambs.Rd
@@ -3,7 +3,8 @@
 \docType{data}
 \name{lambs}
 \alias{lambs}
-\title{a dataset containing the WoW weighing for 4 animals of 1296 observations}
+\title{a dataset containing the WoW weighing for 4 animals of 1296 observations,
+https://doi.org/10.1016/j.compag.2018.08.022}
 \format{
 a data.frame
 \describe{
diff --git a/man/merinos1.Rd b/man/merinos1.Rd
index 18954359fef352c8abb26b6bdefe170fc7a815cd..8bedaa5415a7dd5b828159d5a4e979ce10947b4f 100644
--- a/man/merinos1.Rd
+++ b/man/merinos1.Rd
@@ -3,7 +3,8 @@
 \docType{data}
 \name{merinos1}
 \alias{merinos1}
-\title{a dataset containing the WoW weighing for one animal (merinos lamb) of 397 observations}
+\title{a dataset containing the WoW weighing for one animal (merinos lamb) of 397 
+observations. https://doi.org/10.1016/j.compag.2018.08.022}
 \format{
 a data.frame
 \describe{
diff --git a/man/merinos2.Rd b/man/merinos2.Rd
index d10c3206da9fa4938cb29caf72789a50a876c930..499b44453e81c29ba9da395895ea9707745311d2 100644
--- a/man/merinos2.Rd
+++ b/man/merinos2.Rd
@@ -4,7 +4,7 @@
 \name{merinos2}
 \alias{merinos2}
 \title{a dataset containing the WoW weighing for one animal (merinos lamb) of 345
-observations, difficult to model}
+observations, difficult to model. https://doi.org/10.1016/j.compag.2018.08.022}
 \format{
 a data.frame
 \describe{
diff --git a/man/spring1.Rd b/man/spring1.Rd
index 185c8ab6572069e6074a3c5c4a4295a4d68bb9e6..8f8e1d4baf5526c1427e3b221d51b4911737cbd6 100644
--- a/man/spring1.Rd
+++ b/man/spring1.Rd
@@ -3,7 +3,8 @@
 \docType{data}
 \name{spring1}
 \alias{spring1}
-\title{a dataset containing the WoW weighing for one animal of 203 observations}
+\title{a dataset containing the WoW weighing for one animal of 203 observations.
+https://doi.org/10.1016/j.compag.2018.08.022}
 \format{
 a data.frame
 \describe{
diff --git a/vignettes/HowTo.Rmd b/vignettes/HowTo.Rmd
index 882ec6c80237d52ec6057db903e6bcb7dbbb4b1b..f03e8ac0bd5b369f6dd7acc2298715522d7398e7 100644
--- a/vignettes/HowTo.Rmd
+++ b/vignettes/HowTo.Rmd
@@ -58,9 +58,9 @@ dim(spring1)
 head(spring1)
 ```
 
-The range weight of this animal is between 30 and 75 kg and must be given in the initial parameters of the `kfino_fit()`function.
+The range weight of this animal is between 30 and 75 kg and must be given in `param`, a list of initial parameters to include in the `kfino_fit()` function call.
 
-The user can either perform an outlier detection (and prediction) given initial parameters or on optimized initial parameters (on m0, mm and pp):
+The user can either perform an outlier detection (and prediction) given initial parameters or on optimized initial parameters (on m0, mm and pp). `param` list is composed of:
 
 * m0 = (optional) the initial weight, NULL if the user wants to optimize it,
 * mm = (optional) the target weight, NULL if the user wants to optimize it,
@@ -77,7 +77,7 @@ The user can either perform an outlier detection (and prediction) given initial
 # Kfino algorithm on the `spring1` dataset
 ## Parameters (m0, mm and pp) not optimized
 
-If the user chooses to not optimize the initial parameters, all the list must be completed according to expert knowledge of the data set.
+If the user chooses to not optimize the initial parameters, all the list must be completed according to expert knowledge of the data set. Here, the user supposes that the initial weight is around 41 and the target one around 45.
 
 ```{r,error=TRUE}
 # --- Without Optimisation on parameters
@@ -96,8 +96,39 @@ param2<-list(m0=41,
 resu2<-kfino_fit(datain=spring1,
               Tvar="dateNum",Yvar="Poids",
               param=param2,
-              doOptim=FALSE)     
+              doOptim=FALSE,
+              verbose=TRUE)     
+```
+
+resu2 is a list of 3 elements:
+
+* detectOutlier: The whole input data set with the detected outliers flagged and the prediction of the analyzed variable. the following columns are joined to the columns present in the input data set:
+
+    - prediction: the parameter of interest - Yvar - predicted
+    - label_pred: the probability of the value being well predicted
+    - lwr: lower bound of the confidence interval of the predicted value
+    - upr: upper bound of the confidence interval of the predicted value
+    - flag: flag of the value (OK value, KO value (outlier), OOR value
+        (out of range values defined by the user in `kfino_fit`)
+
+* PredictionOK: A subset of `detectOutlier` data set with the predictions 
+        of the analyzed variable on possible values (OK and KO values)
+* kfino.results: kfino results (a list of vectors, prediction, probability to be an outlier , likelihood, confidence interval of the prediction and the flag of the data) on input parameters that were optimized if the user chooses this option
+
+```{r}
+# structure of detectOutlier data set
+str(resu2$detectOutlier)
 
+# head of PredictionOK data set
+head(resu2$PredictionOK)
+
+# structure of kfino.results list
+str(resu2$kfino.results)
+```
+
+Using the `kfino_plot()`function allows the user to visualize the results:
+
+```{r}
 # flags are qualitative
 kfino_plot(resuin=resu2,typeG="quali",
             Tvar="Day",Yvar="Poids",Ident="IDE")
@@ -107,6 +138,8 @@ kfino_plot(resuin=resu2,typeG="quanti",
             Tvar="Day",Yvar="Poids",Ident="IDE")
 ```
 
+
+
 ## Parameters (m0, mm and pp) optimized
 
 If the user chooses to optimize the initial parameters, m0, mm and pp must be set to NULL.
@@ -127,7 +160,9 @@ param1<-list(m0=NULL,
 
 resu1<-kfino_fit(datain=spring1,
               Tvar="dateNum",Yvar="Poids",
-              doOptim=TRUE,param=param1)  
+              param=param1,
+              doOptim=TRUE,
+              verbose=TRUE)  
 
 # flags are qualitative
 kfino_plot(resuin=resu1,typeG="quali",
@@ -178,7 +213,8 @@ param3<-list(m0=NULL,
 
 resu3<-kfino_fit(datain=merinos1,
               Tvar="dateNum",Yvar="Poids",
-              doOptim=TRUE,param=param3)      
+              doOptim=TRUE,param=param3,
+              verbose=TRUE)      
 
 # flags are qualitative
 kfino_plot(resuin=resu3,typeG="quali",
diff --git a/vignettes/implementedMethods.Rmd b/vignettes/implementedMethods.Rmd
index 16edede9e3d256435edea7bf72b6cfd5e4c099ad..90e99d816888d953457ffbbb8b57916a84f2fe6a 100644
--- a/vignettes/implementedMethods.Rmd
+++ b/vignettes/implementedMethods.Rmd
@@ -71,7 +71,8 @@ param1<-list(m0=NULL,
 ```{r,error=TRUE}
 resu1<-kfino_fit(datain=spring1,
               Tvar="dateNum",Yvar="Poids",
-              doOptim=TRUE,method="ML",param=param1)  
+              doOptim=TRUE,method="ML",param=param1,
+              verbose=TRUE)  
 
 # flags are qualitative
 kfino_plot(resuin=resu1,typeG="quali",
@@ -92,7 +93,8 @@ kfino_plot(resuin=resu1,typeG="prediction",
 
 resu2<-kfino_fit(datain=spring1,
               Tvar="dateNum",Yvar="Poids",
-              doOptim=TRUE,method="EM",param=param1)  
+              doOptim=TRUE,method="EM",param=param1,
+              verbose=TRUE)  
 
 # flags are qualitative
 kfino_plot(resuin=resu2,typeG="quali",
diff --git a/vignettes/multipleFit.Rmd b/vignettes/multipleFit.Rmd
index 99c681d78429b7639cad671d7815666674edb2c2..a4f6f691a0b0dde9aac3b3dfebc200d03f0fc81b 100644
--- a/vignettes/multipleFit.Rmd
+++ b/vignettes/multipleFit.Rmd
@@ -28,6 +28,11 @@ library(parallel)
 library(doParallel)
 ```
 
+This vignette shows how to use parallelization on a data set containing a set of animals weighted over time with the walk-over-weighing system. 
+The `lambs` data set is included in the **kfino** package and can be loaded using the `data()` function. 
+
+We use the **parallel** and **doParallel** libraries to accelerate the computing time.
+
 ```{r}
 data(lambs)
 myIDE<-unique(lambs$IDE)
@@ -35,11 +40,6 @@ myIDE<-unique(lambs$IDE)
 print(myIDE)
 ```
 
-This vignette shows how to use parallelization on a data set containing a set of animals weighted over time with the walk-over-weighing system. 
-The `lambs` data set is included in the **kfino** package and can be loaded using the `data()` function. 
-
-We use the **parallel** and **doParallel** libraries to accelerate the computing time.
-
 # Without parallelization
 
 ```{r,error=TRUE}