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

@@ -1781,60 +1781,6 @@ 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"))