File R-QTLEMM.spec of Package R-QTLEMM

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File R-QTLEMM.spec of Package R-QTLEMM

# Automatically generated by CRAN2OBS
# 
# Spec file for package QTLEMM 
# 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) 2024 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. 
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# Please submit bugfixes or comments via http://bugs.opensuse.org/ 
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%global packname  QTLEMM 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        1.5.4 
Release:        0 
Summary:        QTL Mapping and Hotspots Detection 
Group:          Development/Libraries/Other 
License:        GPL-2 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         QTLEMM_1.5.4.tar.gz 
Requires:       R-base 
Requires:	R-mvtnorm
Requires:	R-gtools
 
# %%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-mvtnorm
BuildRequires: 	R-gtools
 
%description 
For QTL mapping, this package comprises several functions designed to 
execute diverse tasks, such as simulating or analyzing data, 
calculating significance thresholds, and visualizing QTL mapping 
results. The single-QTL or multiple-QTL method, which enables the 
fitting and comparison of various statistical models, is employed to 
analyze the data for estimating QTL parameters. The models encompass 
linear regression, permutation tests, normal mixture models, and 
truncated normal mixture models. The Gaussian stochastic process is 
utilized to compute significance thresholds for QTL detection on a 
genetic linkage map within experimental populations. Two types of data, 
complete genotyping, and selective genotyping data from various 
experimental populations, including backcross, F2, recombinant inbred 
(RI) populations, and advanced intercrossed (AI) populations, are 
considered in the QTL mapping analysis. For QTL hotspot detection, 
statistical methods can be developed based on either utilizing 
individual-level data or summarized data. We have proposed a 
statistical framework capable of handling both individual-level data 
and summarized QTL data for QTL hotspot detection. Our statistical 
framework can overcome the underestimation of thresholds resulting from 
ignoring the correlation structure among traits. Additionally, it can 
identify different types of hotspots with minimal computational cost 
during the detection process. Here, we endeavor to furnish the R codes 
for our QTL mapping and hotspot detection methods, intended for general 
use in genes, genomics, and genetics studies. The QTL mapping methods 
for the complete and selective genotyping designs are based on the 
multiple interval mapping (MIM) model proposed by Kao, C.-H. , Z.-B. 
Zeng and R. D. Teasdale (1999) <doi: 10.1534/genetics.103.021642> and 
H.-I Lee, H.-A. Ho and C.-H. Kao (2014) <doi: 
10.1534/genetics.114.168385>, respectively. The QTL hotspot detection 
analysis is based on the method by Wu, P.-Y., M.-.H. Yang, and C.-H. 
Kao (2021) <doi: 10.1093/g3journal/jkab056>. 
 
%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
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/extdata
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog

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