Update links in README

README.md

@@ -14,15 +14,15 @@ For usage examples see vignettes in inst/doc or use the built-in help after inst
 `?sctransform::vst`  
 
 Available vignettes:  
-[Variance stabilizing transformation](https://rawgit.com/ChristophH/sctransform/develop/supplement/variance_stabilizing_transformation.html)  
-[Using sctransform in Seurat](https://rawgit.com/ChristophH/sctransform/develop/supplement/seurat.html)  
+[Variance stabilizing transformation](https://rawgit.com/ChristophH/sctransform/master/supplement/variance_stabilizing_transformation.html)  
+[Using sctransform in Seurat](https://rawgit.com/ChristophH/sctransform/master/supplement/seurat.html)  
 
 ## News
-The latest version of `sctransform` now supports the [glmGamPoi](https://github.com/const-ae/glmGamPoi) package to speed up the model fitting step. You can see more about the different methods supported and how they compare in terms of results and speed [in this new vignette](https://rawgit.com/ChristophH/sctransform/develop/supplement/method_comparison.html).
+The latest version of `sctransform` now supports the [glmGamPoi](https://github.com/const-ae/glmGamPoi) package to speed up the model fitting step. You can see more about the different methods supported and how they compare in terms of results and speed [in this new vignette](https://rawgit.com/ChristophH/sctransform/master/supplement/method_comparison.html).
 
-Also note that default theta regularization is now based on overdispersion factor (`1 + m / theta` where m is the geometric mean of the observed counts) not `log10(theta)`. The old behavior is still available via `theta_regularization` parameter. You can see how this changes (or doesn't change) the results [in this new vignette](https://rawgit.com/ChristophH/sctransform/develop/supplement/theta_regularization.html).
+Also note that default theta regularization is now based on overdispersion factor (`1 + m / theta` where m is the geometric mean of the observed counts) not `log10(theta)`. The old behavior is still available via `theta_regularization` parameter. You can see how this changes (or doesn't change) the results [in this new vignette](https://rawgit.com/ChristophH/sctransform/master/supplement/theta_regularization.html).
 
-For a detailed change log have a look at the file [NEWS.md](https://github.com/ChristophH/sctransform/blob/develop/NEWS.md)
+For a detailed change log have a look at the file [NEWS.md](https://github.com/ChristophH/sctransform/blob/master/NEWS.md)
 
 
 ## Reference