Fix some links

vignettes/articles/nearest-neighbors-format.Rmd

@@ -145,7 +145,7 @@ you passed in, and the list item names will be `precomputed`.
 ## Multiple neighbor data
 
 As discussed under the
-[Mixed Data Types](https://github.com/jlmelville/uwot#mixed-data-types) section,
+[Mixed Data Types](https://jlmelville.github.io/uwot/articles/mixed-data-types.html) article,
 you can apply multiple distance metrics to different parts of matrix or
 data frame input data. if you do this, then `ret_nn` will return all the
 neighbor data. The list under `nn` will now contain as many items as metrics,

vignettes/uwot.Rmd

@@ -289,9 +289,7 @@ cosine distance applied to row-centered vectors.
 
 If you need other metrics, and can generate the nearest neighbor info
 externally, you can pass the data directly to `uwot` via the `nn_method`
-parameter. See the
-[Nearest Neighbor Data Format section](https://github.com/jlmelville/uwot#nearest-neighbor-data-format)
-for more details. Please note that the Hamming support is a lot slower than the
+parameter. Please note that the Hamming support is a lot slower than the
 other metrics. I do not recommend using it if you have more than a few hundred
 features, and even then expect it to take several minutes during the index
 building phase in situations where the Euclidean metric would take only a few
@@ -345,7 +343,7 @@ measurements, and no support for sparse matrix data input.
 However, `uwot` *does* let you pass in nearest neighbor data. So if you have
 access to other nearest neighbor methods, you can generate data that can be
 used with `uwot`. See the
-[Nearest Neighbor Data Format](https://github.com/jlmelville/uwot#nearest-neighbor-data-format)
+[Nearest Neighbor Data Format](https://jlmelville.github.io/uwot/articles/nearest-neighbors-format.html)
 article. Or if you can calculate a distance matrix for your data, you can pass
 it in as `dist` object.