guix_mirror_bot pushed a commit to branch r-team
in repository guix.
commit 8bf9aee1baa5a1cb7d1a5f15d6acb845e4a03280
Author: Ricardo Wurmus <[email protected]>
AuthorDate: Mon Mar 16 22:06:52 2026 +0100
gnu: Remove r-swne.
This package has been abandoned years ago. It depends on liger, which has
been removed from CRAN on 2024-06-14. It is unlikely to work with the newer
version of rliger.
* gnu/packages/statistics.scm (r-swne): Remove variable.
Change-Id: Ie7d7b3c9e1726b8219ad816c4b8db7621708c3b2
---
gnu/packages/statistics.scm | 54 ---------------------------------------------
1 file changed, 54 deletions(-)
diff --git a/gnu/packages/statistics.scm b/gnu/packages/statistics.scm
index 2be03b8180..7c6c56651b 100644
--- a/gnu/packages/statistics.scm
+++ b/gnu/packages/statistics.scm
@@ -1785,60 +1785,6 @@ 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-rliger
- 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 r-languageserver
(let ((commit "004da9388f9b19990f031c8dc9b527fb406378ba")
(revision "1"))
