File R-kdps.spec of Package R-kdps
# Automatically generated by CRAN2OBS
#
# Spec file for package kdps
# This file is auto-generated using information in the package source,
# esp. Description and Summary. Improvements in that area should be
# discussed with upstream.
#
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%global packname kdps
%global rlibdir %{_libdir}/R/library
Name: R-%{packname}
Version: 1.0.0
Release: 0
Summary: Kinship Decouple and Phenotype Selection (KDPS)
Group: Development/Libraries/Other
License: MIT + file LICENSE
URL: http://cran.r-project.org/web/packages/%{packname}
Source: kdps_1.0.0.tar.gz
Requires: R-base
Requires: R-data.table
Requires: R-dplyr
Requires: R-progress
Requires: R-tibble
Requires: R-cli
Requires: R-generics
Requires: R-glue
Requires: R-lifecycle
Requires: R-magrittr
Requires: R-pillar
Requires: R-R6
Requires: R-rlang
Requires: R-tidyselect
Requires: R-vctrs
Requires: R-crayon
Requires: R-hms
Requires: R-prettyunits
Requires: R-pkgconfig
Requires: R-utf8
Requires: R-withr
# %%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-data.table
BuildRequires: R-dplyr
BuildRequires: R-progress
BuildRequires: R-tibble
BuildRequires: R-cli
BuildRequires: R-generics
BuildRequires: R-glue
BuildRequires: R-lifecycle
BuildRequires: R-magrittr
BuildRequires: R-pillar
BuildRequires: R-R6
BuildRequires: R-rlang
BuildRequires: R-tidyselect
BuildRequires: R-vctrs
BuildRequires: R-crayon
BuildRequires: R-hms
BuildRequires: R-prettyunits
BuildRequires: R-pkgconfig
BuildRequires: R-utf8
BuildRequires: R-withr
Suggests: R-devtools
Suggests: R-knitr
Suggests: R-rmarkdown
%description
A phenotype-aware algorithm for resolving cryptic relatedness in
genetic studies. It removes related individuals based on kinship or
identity-by-descent (IBD) scores while prioritizing subjects with
phenotypes of interest. This approach helps maximize the retention of
informative subjects, particularly for rare or valuable traits, and
improves statistical power in genetic and epidemiological studies. KDPS
supports both categorical and quantitative phenotypes, composite
scoring, and customizable pruning strategies using a fuzziness
parameter. Benchmark results show improved phenotype retention and high
computational efficiency on large-scale datasets like the UK Biobank.
Methods used include Manichaikul et al. (2010)
<doi:10.1093/bioinformatics/btq559> for kinship estimation, Purcell et
al. (2007) <doi:10.1086/519795> for IBD estimation, and Bycroft et al.
(2018) <doi:10.1038/s41586-018-0579-z> for UK Biobank data reference.
%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}
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%license %{rlibdir}/%{packname}/LICENSE
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%{rlibdir}/%{packname}/R
%doc %{rlibdir}/%{packname}/doc
%{rlibdir}/%{packname}/extdata
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
%changelog