diff options
| author | Ricardo Wurmus <rekado@elephly.net> | 2022-02-08 23:55:38 +0100 |
|---|---|---|
| committer | Ricardo Wurmus <rekado@elephly.net> | 2022-02-09 09:02:24 +0100 |
| commit | 61d1d4a3c5420acef48376871021709b00fc9d2f (patch) | |
| tree | d6fcbee18e1fc89672240ac8d2265259f037b2f4 | |
| parent | 98a4da8a13d792188c144f7e31538caa00f90cc3 (diff) | |
| download | guix-patches-61d1d4a3c5420acef48376871021709b00fc9d2f.tar guix-patches-61d1d4a3c5420acef48376871021709b00fc9d2f.tar.gz | |
gnu: Add r-swne.
* gnu/packages/statistics.scm (r-swne): New variable.
| -rw-r--r-- | gnu/packages/statistics.scm | 54 |
1 files changed, 54 insertions, 0 deletions
diff --git a/gnu/packages/statistics.scm b/gnu/packages/statistics.scm index a07540417f..648462be03 100644 --- a/gnu/packages/statistics.scm +++ b/gnu/packages/statistics.scm @@ -6106,6 +6106,60 @@ knowledge integration, designable W and H matrices and multiple forms of regularizations.") (license license:bsd-2)))) +(define-public r-swne + (let ((commit "05fc3ee4e09b2c34d99c69d3b97cece4c1c34143") + (revision "1")) + (package + (name "r-swne") + (version (git-version "0.6.20" revision commit)) + (source + (origin + (method git-fetch) + (uri (git-reference + (url "https://github.com/yanwu2014/swne") + (commit commit))) + (file-name (git-file-name name version)) + (sha256 + (base32 "0crlpg9kclbv4v8250p3086a3lk6f2hcq79psqkdylc1qnrx3kfx")))) + (properties `((upstream-name . "swne"))) + (build-system r-build-system) + (propagated-inputs + (list r-fnn + r-ggplot2 + r-ggrepel + r-hash + r-ica + r-igraph + r-irlba + r-jsonlite + r-liger + r-mass + r-matrix + r-mgcv + r-nnlm ;not listed but required at install time + r-plyr + r-proxy + r-rcolorbrewer + r-rcpp + r-rcpparmadillo + r-rcppeigen + r-reshape + r-reshape2 + r-snow + r-umap + r-usedist)) + (home-page "https://github.com/yanwu2014/swne") + (synopsis "Visualize high dimensional datasets") + (description + "@dfn{Similarity Weighted Nonnegative Embedding} (SWNE) is a method for +visualizing high dimensional datasets. SWNE uses Nonnegative Matrix +Factorization to decompose datasets into latent factors, projects those +factors onto 2 dimensions, and embeds samples and key features in 2 dimensions +relative to the factors. SWNE can capture both the local and global dataset +structure, and allows relevant features to be embedded directly onto the +visualization, facilitating interpretation of the data.") + (license license:gpl2)))) + (define-public python-rpy2 (package (name "python-rpy2") |