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

问题描述

R至少有两个稀疏矩阵包.我正在研究它们，因为我正在使用的数据集太大且稀疏，无法以密集表示形式容纳在内存中.我需要基本的线性代数例程，以及能够轻松编写C代码以对其进行操作的功能.哪个库最成熟且最适合使用?

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

• 矩阵具有许多反向依赖性，这意味着它是最常用的.
• SparseM ，它没有那么多的反向部门
• 各种图形库可能对此都有自己的(隐式)版本；例如 igraph 和

• 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.

有人对此有经验吗?

通过在 RSeek.org 周围进行搜索， CRAN任务视图是相当权威的，而

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是最常见的矩阵，并且刚刚被接受为R标准安装(自2.9.0版开始)，因此应该广泛可用.

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

基数矩阵: https://stat.ethz.ch/pipermail/r-announce/2009 /000499.html

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