最成熟的 R 稀疏矩阵包? [英] Most mature sparse matrix package for R?

问题描述

R 至少有两个稀疏矩阵包.我正在研究这些，因为我正在处理的数据集太大且太稀疏而无法以密集的表示方式放入内存.我想要基本的线性代数例程，以及轻松编写 C 代码来操作它们的能力.哪个库最成熟，最好用?

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?

到目前为止我已经找到了

So far I've found

• Matrix 具有许多反向依赖关系，这意味着它是最常用的一种.
• SparseM 没有那么多反向 deps.
• 各种图形库可能都有自己的(隐式)版本；例如igraph 和 network(后者是 statnet).这些对于我的需求来说太专业了.

• 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.

有人有这方面的经验吗?

Anyone have experience with this?

通过稍微搜索 RSeek.org，Matrix 包似乎是最常提到的一个.我经常认为 CRAN Task Views 相当权威，Multivariate Task View 提到了 Matrix 和 SparseM.

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.

推荐答案

Matrix 是最常见的，也刚刚被接受 R 标准安装(从 2.9.0 开始)，所以应该可以广泛使用.

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

基中的矩阵:https://stat.ethz.ch/pipermail/r-announce/2009/000499.html

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