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| -rw-r--r-- | gnu/packages/cran.scm | 32 |
1 files changed, 32 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm index 7e2539da81..82fd465d79 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -10618,3 +10618,35 @@ the local machine to, say, distributed processing on a remote compute cluster.") can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.") (license license:gpl2+))) + +(define-public r-rsvd + (package + (name "r-rsvd") + (version "1.0.0") + (source + (origin + (method url-fetch) + (uri (cran-uri "rsvd" version)) + (sha256 + (base32 + "0vjhrvnkl9rmvl8sv2kac5sd10z3fgxymb676ynxzc2pmhydy3an")))) + (build-system r-build-system) + (propagated-inputs + `(("r-matrix" ,r-matrix))) + (home-page "https://github.com/erichson/rSVD") + (synopsis "Randomized singular value decomposition") + (description + "Low-rank matrix decompositions are fundamental tools and widely used for +data analysis, dimension reduction, and data compression. Classically, highly +accurate deterministic matrix algorithms are used for this task. However, the +emergence of large-scale data has severely challenged our computational +ability to analyze big data. The concept of randomness has been demonstrated +as an effective strategy to quickly produce approximate answers to familiar +problems such as the @dfn{singular value decomposition} (SVD). This package +provides several randomized matrix algorithms such as the randomized singular +value decomposition (@code{rsvd}), randomized principal component +analysis (@code{rpca}), randomized robust principal component +analysis (@code{rrpca}), randomized interpolative decomposition (@code{rid}), +and the randomized CUR decomposition (@code{rcur}). In addition several plot +functions are provided.") + (license license:gpl3+))) |