File R-missForest.spec of Package R-missForest

# Automatically generated by CRAN2OBS
# 
# Spec file for package missForest 
# 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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# remain the property of their copyright owners, unless otherwise agreed 
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# file, is the same license as for the pristine package itself (unless the 
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%global packname  missForest 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        1.6.1 
Release:        0 
Summary:        Nonparametric Missing Value Imputation using Random Forest 
Group:          Development/Libraries/Other 
License:        GPL (>= 2) 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         missForest_1.6.1.tar.gz 
Requires:       R-base 
Requires:	R-randomForest
Requires:	R-ranger
Requires:	R-foreach
Requires:	R-iterators
Requires:	R-itertools
Requires:	R-doRNG
Requires:	R-Rdpack
Requires:	R-rngtools
Requires:	R-Rcpp
Requires:	R-RcppEigen
Requires:	R-rbibutils
Requires:	R-digest
 
# %%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-randomForest
BuildRequires: 	R-ranger
BuildRequires: 	R-foreach
BuildRequires: 	R-iterators
BuildRequires: 	R-itertools
BuildRequires: 	R-doRNG
BuildRequires: 	R-Rdpack
BuildRequires: 	R-rngtools
BuildRequires: 	R-Rcpp-devel
BuildRequires: 	R-RcppEigen-devel
BuildRequires: 	R-rbibutils
BuildRequires: 	R-digest
 
Suggests:	R-doParallel
Suggests:	R-knitr
Suggests:	R-rmarkdown
%description 
The function 'missForest' in this package is used to impute missing 
values particularly in the case of mixed-type data. It uses a random 
forest (via 'ranger' or 'randomForest') trained on the observed values 
of a data matrix to predict the missing values. It can be used to 
impute continuous and/or categorical data including complex 
interactions and non-linear relations. It yields an out-of-bag (OOB) 
imputation error estimate without the need of a test set or elaborate 
cross-validation. It can be run in parallel to save computation time. 
 
%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
%doc %{rlibdir}/%{packname}/doc
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog 
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