File R-sharp.spec of Package R-sharp

# Automatically generated by CRAN2OBS
# 
# Spec file for package sharp 
# This file is auto-generated using information in the package source, 
# esp. Description and Summary. Improvements in that area should be 
# discussed with upstream. 
# 
# Copyright (c) 2026 SUSE LINUX GmbH, Nuernberg, Germany. 
# 
# All modifications and additions to the file contributed by third parties 
# remain the property of their copyright owners, unless otherwise agreed 
# upon. The license for this file, and modifications and additions to the 
# file, is the same license as for the pristine package itself (unless the 
# license for the pristine package is not an Open Source License, in which 
# case the license is the MIT License). An "Open Source License" is a 
# license that conforms to the Open Source Definition (Version 1.9) 
# published by the Open Source Initiative. 
#  
# Please submit bugfixes or comments via http://bugs.opensuse.org/ 
# 
 
%global packname  sharp 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        1.4.8 
Release:        0 
Summary:        Stability-enHanced Approaches using Resampling Procedures 
Group:          Development/Libraries/Other 
License:        GPL (>= 3) 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         sharp_1.4.8.tar.gz 
Requires:       R-base 
Requires:	R-fake
Requires:	R-abind
Requires:	R-beepr
Requires:	R-future
Requires:	R-future.apply
Requires:	R-glassoFast
Requires:	R-glmnet
Requires:	R-igraph
Requires:	R-mclust
Requires:	R-nloptr
Requires:	R-plotrix
Requires:	R-Rdpack
Requires:	R-withr
Requires:	R-audio
Requires:	R-digest
Requires:	R-globals
Requires:	R-listenv
Requires:	R-parallelly
Requires:	R-foreach
Requires:	R-shape
Requires:	R-Rcpp
Requires:	R-RcppEigen
Requires:	R-cli
Requires:	R-lifecycle
Requires:	R-magrittr
Requires:	R-pkgconfig
Requires:	R-rlang
Requires:	R-vctrs
Requires:	R-cpp11
Requires:	R-rbibutils
Requires:	R-iterators
Requires:	R-glue
 
# %%if 0%%{?sle_version} > 120400 || 0%%{?is_opensuse} 
# # Three others commonly needed 
# BuildRequires:  tex(ae.sty) 
# BuildRequires:  tex(fancyvrb.sty) 
# BuildRequires:  tex(inconsolata.sty) 
# BuildRequires:  tex(natbib.sty) 
# %else 
# BuildRequires:  texlive 
# %endif 
# BuildRequires:  texinfo 
BuildRequires:  fdupes 
BuildRequires:  R-base 
BuildRequires: 	R-fake
BuildRequires: 	R-abind
BuildRequires: 	R-beepr
BuildRequires: 	R-future
BuildRequires: 	R-future.apply
BuildRequires: 	R-glassoFast
BuildRequires: 	R-glmnet
BuildRequires: 	R-igraph
BuildRequires: 	R-mclust
BuildRequires: 	R-nloptr
BuildRequires: 	R-plotrix
BuildRequires: 	R-Rdpack
BuildRequires: 	R-withr
BuildRequires: 	R-audio
BuildRequires: 	R-digest
BuildRequires: 	R-globals
BuildRequires: 	R-listenv
BuildRequires: 	R-parallelly
BuildRequires: 	R-foreach
BuildRequires: 	R-shape
BuildRequires: 	R-Rcpp-devel
BuildRequires: 	R-RcppEigen-devel
BuildRequires: 	R-cli
BuildRequires: 	R-lifecycle
BuildRequires: 	R-magrittr
BuildRequires: 	R-pkgconfig
BuildRequires: 	R-rlang
BuildRequires: 	R-vctrs
BuildRequires: 	R-cpp11-devel
BuildRequires: 	R-rbibutils
BuildRequires: 	R-iterators
BuildRequires: 	R-glue
 
Suggests:	R-corpcor
Suggests:	R-dbscan
Suggests:	R-elasticnet
Suggests:	R-gglasso
Suggests:	R-mixOmics
Suggests:	R-OpenMx
Suggests:	R-RCy3
Suggests:	R-randomcoloR
Suggests:	R-rCOSA
Suggests:	R-rmarkdown
Suggests:	R-sgPLS
Suggests:	R-sparcl
Suggests:	R-testthat
Suggests:	R-visNetwork
%description 
In stability selection (N Meinshausen, P Bühlmann (2010) 
<doi:10.1111/j.1467-9868.2010.00740.x>) and consensus clustering (S 
Monti et al (2003) <doi:10.1023/A:1023949509487>), resampling 
techniques are used to enhance the reliability of the results. In this 
package (B Bodinier et al (2025) <doi:10.18637/jss.v112.i05>), 
hyper-parameters are calibrated by maximising model stability, which is 
measured under the null hypothesis that all selection (or 
co-membership) probabilities are identical (B Bodinier et al (2023a) 
<doi:10.1093/jrsssc/qlad058> and B Bodinier et al (2023b) 
<doi:10.1093/bioinformatics/btad635>). Functions are readily 
implemented for the use of LASSO regression, sparse PCA, sparse (group) 
PLS or graphical LASSO in stability selection, and hierarchical 
clustering, partitioning around medoids, K means or Gaussian mixture 
models in consensus clustering. 
 
%prep 
%setup -q -c -n %{packname} 
# the next line is needed, because we build without --clean in between two packages 
rm -rf ~/.R  
 
 
%build 
 
%install 
mkdir -p %{buildroot}%{rlibdir} 
%{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname} 
test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so) 
rm -f %{buildroot}%{rlibdir}/R.css 
%fdupes -s %{buildroot}%{rlibdir} 
 
#%%check 
#%%{_bindir}/R CMD check %%{packname} 
 
%files 
%dir %{rlibdir}/%{packname} 
%{rlibdir}/%{packname}/CITATION
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/REFERENCES.bib
%{rlibdir}/%{packname}/WORDLIST
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog