Basic linear algebra on spark matrices

I am trying to run some basic linear algebra operations (specifically transpose, dot product, and inverse) on a matrix stored as a spark RowMatrix as described herehere (using the Python API). Following the example in the docs (for my case I will have many more rows in the matrix, hence the need for Spark), suppose I have something like this:

from pyspark.mllib.linalg.distributed import RowMatrix
# Create an RDD of vectors.
rows = sc.parallelize([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
# Create a RowMatrix from an RDD of vectors.
mat = RowMatrix(rows)

Given such a distributed matrix, are there existing routines for doing matrix transpose and dot product, e.g:

dot(mat.T,mat)

or matrix inverse?

inverse(mat)

I can't seem to find anything in the documentation about this. Looking for either (a) a pointer to the relevant docs or (b) a method for implementing this myself.

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