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.
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
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.
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.
来源：https://stackoverflow.com/questions/1167448/most-mature-sparse-matrix-package-for-r