v1.0.9.62 URL corrected in vignettes in order to pass the cran check

DESCRIPTION

 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")),

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
 

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'}

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.