From eb3fce3a0034759876aebc1af0b058863516845a Mon Sep 17 00:00:00 2001 From: Ricardo Wurmus Date: Mon, 16 Mar 2026 22:06:52 +0100 Subject: [PATCH] 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 e39439c9cc..52179238cb 100644 --- a/gnu/packages/statistics.scm +++ b/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"))