diff --git a/R_buildignore/developer_commands.R b/R_buildignore/developer_commands.R
index 393d850..26ed77d 100644
--- a/R_buildignore/developer_commands.R
+++ b/R_buildignore/developer_commands.R
@@ -37,8 +37,12 @@ devtools::check()  # run_dont_test = TRUE
 rcmdcheck::rcmdcheck()  # build_args = "--run-donttest"
 devtools::build()
 #devtools::revdep(pkg = "fitHeavyTail")  # to check reverse dependencies
-#devtools::check_win_release()  #to check under windows
+#devtools::check_win_release()   # to check under Windows
+#devtools::check_mac_release()   # to check under Mac OS
+#rhub::check_for_cran(platform = "macos-highsierra-release-cran")
 #R CMD build .  # this is to generate tarball
 #R CMD check fitHeavyTail_0.2.0.tar.gz --as-cran --run-donttest  # this is before submission to CRAN
 #R CMD install fitHeavyTail_0.2.0.tar.gz
 #submit the tarball directly via the webform: https://cran.r-project.org/submit.html
+
+
diff --git a/inst/CITATION b/inst/CITATION
index 4674f56..2ab7277 100644
--- a/inst/CITATION
+++ b/inst/CITATION
@@ -1,6 +1,6 @@
 citHeader("To cite 'fitHeavyTail' in publications, please use:")
 
-bibentry(entry = "Manual",
+bibentry(bibtype      = "Manual",
          title        = "{fitHeavyTail: Mean and Covariance Matrix Estimation under Heavy Tails}",
          author       = c(as.person("D. P. Palomar"),
                           as.person("R. Zhou"),
@@ -17,7 +17,7 @@ bibentry(entry = "Manual",
                  "https://CRAN.R-project.org/package=fitHeavyTail")
 )
 
-bibentry(entry = "Article",
+bibentry(bibtype      = "Article",
          title        = "Regularized Tyler’s scatter estimator: Existence, uniqueness, and algorithms",
          author       = c(as.person("Y. Sun"),
                           as.person("P. Babu"),
@@ -36,7 +36,7 @@ bibentry(entry = "Article",
                  "https://doi.org/10.1109/TSP.2014.2348944")
 )
 
-bibentry(entry = "Article",
+bibentry(bibtype      = "Article",
          title        = "Regularized robust estimation of mean and covariance matrix under heavy-tailed distributions",
          author       = c(as.person("Y. Sun"),
                           as.person("P. Babu"),
@@ -55,7 +55,7 @@ bibentry(entry = "Article",
                  "https://doi.org/10.1109/TSP.2015.2417513")
 )
 
-bibentry(entry = "Article",
+bibentry(bibtype      = "Article",
          title        = "Shrinking the Eigenvalues of M-estimators of Covariance Matrix",
          author       = c(as.person("E. Ollila"),
                           as.person("D. P. Palomar"),
diff --git a/vignettes/CovarianceEstimationHeavyTail.Rmd b/vignettes/CovarianceEstimationHeavyTail.Rmd
index 4c23ca5..53d258f 100644
--- a/vignettes/CovarianceEstimationHeavyTail.Rmd
+++ b/vignettes/CovarianceEstimationHeavyTail.Rmd
@@ -124,7 +124,7 @@ sum((fitted_3factors$cov - Sigma_cov)^2)
 
 To get a visual idea of the robustness, we can plot the shapes of the covariance matrices (true and estimated ones) on two dimensions. Observe how the heavy-tailed estimation follows the true one more closely than the sample covariance matrix:
 
-```{r scatter-plots, echo=FALSE, fig.width=10, fig.height=6, out.width="100%"}
+```{r scatter-plots, echo=FALSE, fig.width=10, fig.height=6, out.width="90%"}
 # fig.cap="Sample covariance matrix vs robust estimator."
 library(mvtnorm)
 library(ellipse)