diff --git a/cran-comments.md b/cran-comments.md
index 6f07de8..c159120 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -11,4 +11,6 @@ This is a patch release (from 0.2.1 to 0.2.2)
 
 0 errors | 0 warnings | 0 note
 
+* Second submission for the version 0.2.2. I've corrected the (possibly) invalid URLs.
+
 * I've used `\donttest{}` in `double_decay()` and `view_on_copula()` examples because they take more than 5 secs to run.
diff --git a/vignettes/views.Rmd b/vignettes/views.Rmd
index 33ca7f4..54c5d44 100644
--- a/vignettes/views.Rmd
+++ b/vignettes/views.Rmd
@@ -383,7 +383,7 @@ In which $x_j$ is a yet to be defined probability vector; $V_{j,k}$ is a matrix
 
 When $j = z$, the panels $V_{j,k}$ and $\hat{V}_{z,k}$ have the same number of rows and the dimensions in both sides of the restrictions match. However, it's possible to set $z \ge j$ to simulate a larger panel for $\hat{V}_{z, k}$. Keep in mind though that, if $z \ne j$, two _prior_ probabilities will have to be specified: one for $p_j$ (the objective function) and one for $p_z$ (the _views_). 
 
-Continuing on the example, consider the margins of `x` can be approximated by a symmetric multivariate t-distribution. If this is the case, the estimation can be conducted by the amazing [ghyp](www.cran.r-project.org/web/packages/ghyp/index.html) package, that covers the entire family of [generalized hyperbolic distributions](https://en.wikipedia.org/wiki/Generalised_hyperbolic_distribution):
+Continuing on the example, consider the margins of `x` can be approximated by a symmetric multivariate t-distribution. If this is the case, the estimation can be conducted by the amazing `ghyp` package, that covers the entire family of [generalized hyperbolic distributions](https://en.wikipedia.org/wiki/Generalised_hyperbolic_distribution):
 
 <!-- The optimization is assisted by the Expectation Maximization (EM) Algorithm that handle univariate as well as multivariate time series: -->
 
@@ -468,7 +468,7 @@ $$ \sum_{j=1}^J x_j U_{j,k}U_{j,l}U_{j,i}  =  \sum_{j=1}^J p_j \hat{U}_{j,k}\hat
 
 In which, the first restriction matches the first moment of the uniform distribution; the second and third restrictions pair the cross-moments of the empirical copula, $U$, with the simulated copula, $\hat{U}$; $x_j$ is a yet to be discovered _posterior_ distribution; $p_z$ is a _prior_ probability; When $j = z$, the dimensions of $p_j$ and $p_z$ match.
 
-Among many of the available copulas, say the investor wants to model the dependence of the market as a [clayton copula](https://en.wikipedia.org/wiki/Copula_(probability_theory)) to ensure the lower [tail dependency](https://en.wikipedia.org/wiki/Tail_dependence) does not go unnoticed. The estimation is simple to implement with the package [copula](www.cran.r-project.org/web/packages/copula/index.html):
+Among many of the available copulas, say the investor wants to model the dependence of the market as a [clayton copula](https://en.wikipedia.org/wiki/Copula_(probability_theory)) to ensure the lower [tail dependency](https://en.wikipedia.org/wiki/Tail_dependence) does not go unnoticed. The estimation is simple to implement with the package `copula`:
 
 ```{r, warning=FALSE, message=FALSE}
 library(copula)