diff --git a/DESCRIPTION b/DESCRIPTION
index 55f17f9235453c486ca67a3cbbc1a48076c193fc..2f8e52f391217bf8a316c46c998fb8db6faf96a1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: airGR
 Type: Package
 Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling
-Version: 1.0.9.61
+Version: 1.0.9.62
 Date: 2017-11-09
 Authors@R: c(
   person("Laurent", "Coron", role = c("aut", "trl")),
diff --git a/NEWS.rmd b/NEWS.rmd
index 2c8c74ad5cbcfedd88b47b7417089b5a34aff196..97bfe0560b8231b746640a4f6e0fa7fd9b31775e 100644
--- a/NEWS.rmd
+++ b/NEWS.rmd
@@ -14,7 +14,7 @@ output:
 
 
 
-### 1.0.9.61 Release Notes (2017-11-09) 
+### 1.0.9.62 Release Notes (2017-11-09) 
 
 #### New features
 
diff --git a/vignettes/V02.1_param_optim.Rmd b/vignettes/V02.1_param_optim.Rmd
index 147510012b9c4f6cbc5a1dfb75327171950c4d0d..42da9a0092f72ddc9f91ed9d06676bf8b498c085 100644
--- a/vignettes/V02.1_param_optim.Rmd
+++ b/vignettes/V02.1_param_optim.Rmd
@@ -123,9 +123,9 @@ The existence of several local minima illustrates the importance of defining an
 Global optimization is most often used when facing a complex response surface, with multiple local mimina.
 Here we use the following R implementation of some popular strategies:
 
-* [DEoptim: differential evolution](https://cran.r-project.org/web/packages/DEoptim/index.html)
-* [hydroPSO: particle swarm](https://cran.r-project.org/web/packages/hydroPSO/index.html)
-* [Rmalschains: memetic algorithms](https://cran.r-project.org/web/packages/Rmalschains/index.html)
+* [DEoptim: differential evolution](https://cran.r-project.org/package=DEoptim)
+* [hydroPSO: particle swarm](https://cran.r-project.org/package=hydroPSO)
+* [Rmalschains: memetic algorithms](https://cran.r-project.org/package=Rmalschains)
 
 ## Differential Evolution
 ```{r, warning=FALSE, results='hide'}
diff --git a/vignettes/V02.2_param_mcmc.Rmd b/vignettes/V02.2_param_mcmc.Rmd
index fb578d89bc8e05b2f27580dd6ba9f6c71d3fdd43..5db9cfa080e8d1aa0c205a0606509bbcfb4f55bb 100644
--- a/vignettes/V02.2_param_mcmc.Rmd
+++ b/vignettes/V02.2_param_mcmc.Rmd
@@ -43,7 +43,7 @@ Please note that this vignette is only for illustration purposes and does not pr
 
 ## Standard Least Squares (SLS) Bayesian inference
 
-We show how to use the DRAM algorithm for SLS Bayesian inference, with the `modMCMC()` function of the [FME](https://cran.r-project.org/web/packages/FME/) package.
+We show how to use the DRAM algorithm for SLS Bayesian inference, with the `modMCMC()` function of the [FME](https://cran.r-project.org/package=FME) package.
 First, we need to define a function that returns twice the opposite of the log-likelihood for a given parameter set.
 
 Nota: in the `RunAirGR4J()` function, the computation of the log-likelihood is simplified in order to ensure a good computing performance. It corresponds to a translation  of the two following lines. 
@@ -118,7 +118,7 @@ mcmcDRAM <- apply(ListIniParam, 2, function(iListIniParam) {
 ## MCMC diagnostics and visualisation tools
 
 There are several diagnostics that can be used to check the convergence of the chains.
-The R package [coda](https://cran.r-project.org/web/packages/coda/index.html) provides several diagnostic tests.
+The R package [coda](https://cran.r-project.org/package=coda) provides several diagnostic tests.
 
 Among others, the Gelman and Rubin's convergence can be used. A value close to 1 suggests acceptable convergence.
 The result will be better with more iterations than 2000. As we kept the iterations during the convergence process, we have to set the `autoburnin` argument to `TRUE` in order to consider only the second half of the series.
@@ -133,7 +133,7 @@ GelRub <- coda::gelman.diag(MultDRAM, autoburnin = TRUE)$mpsrf
 GelRub
 ```
 
-In addition, graphical tools can be used, with for example the [ggmcmc](https://cran.r-project.org/web/packages/ggmcmc/) package.
+In addition, graphical tools can be used, with for example the [ggmcmc](https://cran.r-project.org/package=ggmcmc) package.