Most mature sparse matrix package for R?

问题:

There are at least two sparse matrix packages for R. I'm looking into these because I'm working with datasets that are too big and sparse to fit in memory with a dense representation. I want basic linear algebra routines, plus the ability to easily write C code to operate on them. Which library is the most mature and best to use?

So far I've found

• Matrix which has many reverse dependencies, implying it's the most used one.
• SparseM which doesn't have as many reverse deps.
• Various graph libraries probably have their own (implicit) versions of this; e.g. igraph and network (the latter is part of statnet). These are too specialized for my needs.

Anyone have experience with this?

From searching around RSeek.org a little bit, the Matrix package seems the most commonly mentioned one. I often think of CRAN Task Views as fairly authoritative, and the Multivariate Task View mentions Matrix and SparseM.

回答1:

Matrix is the most common and has also just been accepted R standard installation (as of 2.9.0), so should be broadly available.

Matrix in base: https://stat.ethz.ch/pipermail/r-announce/2009/000499.html

回答2:

In my experience, Matrix is the best supported and most mature of the packages you mention. Its C architecture should also be fairly well-exposed and relatively straightforward to work with.

回答3:

log(x) on a sparse matrix is a bad idea since log(0) isn't defined and most elements of a sparse matrix are zero.

If you would just like to get the log of the non-zero elements, try converting to a triplet sparse representation and taking a log of those values.

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