diff --git a/DESCRIPTION b/DESCRIPTION
index ab69e371aa9bf136c933121a790bf040500c0b1b..14a81b192e89d413338464093f1e0baa9f5586a0 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -10,20 +10,20 @@ Author: Bertrand Cloez [aut],
   Benedicte Fontez [ctr]
 Maintainer: Isabelle Sanchez <isabelle.sanchez@inrae.fr>
 Description: A method for detecting outliers with a Kalman filter on impulsed 
-  noised outliers and prediction on cleaned data. 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 
+  noised outliers and prediction on cleaned data. '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. `ML` 
-  (Maximization Likelihood) and `EM` (Expectation-Maximization algorithm) 
-  algorithms were implemented in `kfino`. The method is described in full 
-  details in the following arxiv preprint: <https://arxiv.org/abs/2208.00961>.
+  are data points that differ significantly from other observations. 'ML' 
+  (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) 
+  algorithms were implemented in 'kfino'. The method is described in full 
+  details in the following arXiv e-Print: <arXiv:2208.00961>.
 License: GPL-3
 Depends: R (>= 4.1.0)
 Encoding: UTF-8
 LazyData: TRUE
 URL: https://forgemia.inra.fr/isabelle.sanchez/kfino
-BugReports: https://forgemia.inra.fr/isabelle.sanchez/kfino/issues
+BugReports: https://forgemia.inra.fr/isabelle.sanchez/kfino/-/issues
 Imports: 
     ggplot2,
     dplyr,
diff --git a/man/kfino.Rd b/man/kfino.Rd
index a2ec5bf77f3b1d02d8953e9adbe37b334cce01f3..86e5301ee8d547e00c6dde58730ef31d80df97d4 100644
--- a/man/kfino.Rd
+++ b/man/kfino.Rd
@@ -8,7 +8,7 @@
 \description{
 \if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
 
-A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. 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. `ML` (Maximization Likelihood) and `EM` (Expectation-Maximization algorithm) algorithms were implemented in `kfino`. The method is described in full details in the following arxiv preprint: \url{https://arxiv.org/abs/2208.00961}.
+A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. '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. 'ML' (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) algorithms were implemented in 'kfino'. The method is described in full details in the following arXiv e-Print: \href{https://arxiv.org/abs/2208.00961}{arXiv:2208.00961}.
 }
 \details{
 xxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx.
@@ -17,7 +17,7 @@ xxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx.
 Useful links:
 \itemize{
   \item \url{https://forgemia.inra.fr/isabelle.sanchez/kfino}
-  \item Report bugs at \url{https://forgemia.inra.fr/isabelle.sanchez/kfino/issues}
+  \item Report bugs at \url{https://forgemia.inra.fr/isabelle.sanchez/kfino/-/issues}
 }
 
 }