File R-polle.spec of Package R-polle
# Automatically generated by CRAN2OBS
#
# Spec file for package polle
# 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) 2025 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 polle
%global rlibdir %{_libdir}/R/library
Name: R-%{packname}
Version: 1.6.0
Release: 0
Summary: Policy Learning
Group: Development/Libraries/Other
License: Apache License (>= 2)
URL: http://cran.r-project.org/web/packages/%{packname}
Source: polle_1.6.0.tar.gz
Requires: R-base
Requires: R-SuperLearner
Requires: R-data.table
Requires: R-lava
Requires: R-future.apply
Requires: R-progressr
Requires: R-policytree
Requires: R-targeted
Requires: R-DynTxRegime
Requires: R-modelObj
Requires: R-kernlab
Requires: R-rgenoud
Requires: R-dfoptim
Requires: R-future
Requires: R-globals
Requires: R-cli
Requires: R-numDeriv
Requires: R-SQUAREM
Requires: R-Rcpp
Requires: R-grf
Requires: R-BH
Requires: R-digest
Requires: R-nnls
Requires: R-gam
Requires: R-cvAUC
Requires: R-R6
Requires: R-abind
Requires: R-mets
Requires: R-optimx
Requires: R-quadprog
Requires: R-rlang
Requires: R-RcppArmadillo
Requires: R-ROCR
Requires: R-listenv
Requires: R-parallelly
Requires: R-foreach
Requires: R-DiceKriging
Requires: R-lmtest
Requires: R-sandwich
Requires: R-RcppEigen
Requires: R-mvtnorm
Requires: R-timereg
Requires: R-nloptr
Requires: R-pracma
Requires: R-iterators
Requires: R-zoo
Requires: R-gplots
Requires: R-gtools
Requires: R-caTools
Requires: R-bitops
# %%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-SuperLearner
BuildRequires: R-data.table
BuildRequires: R-lava
BuildRequires: R-future.apply
BuildRequires: R-progressr
BuildRequires: R-policytree
BuildRequires: R-targeted
BuildRequires: R-DynTxRegime
BuildRequires: R-modelObj
BuildRequires: R-kernlab
BuildRequires: R-rgenoud
BuildRequires: R-dfoptim
BuildRequires: R-future
BuildRequires: R-globals
BuildRequires: R-cli
BuildRequires: R-numDeriv
BuildRequires: R-SQUAREM
BuildRequires: R-Rcpp-devel
BuildRequires: R-grf
BuildRequires: R-BH-devel
BuildRequires: R-digest
BuildRequires: R-nnls
BuildRequires: R-gam
BuildRequires: R-cvAUC
BuildRequires: R-R6
BuildRequires: R-abind
BuildRequires: R-mets
BuildRequires: R-optimx
BuildRequires: R-quadprog
BuildRequires: R-rlang
BuildRequires: R-RcppArmadillo-devel
BuildRequires: R-ROCR
BuildRequires: R-listenv
BuildRequires: R-parallelly
BuildRequires: R-foreach
BuildRequires: R-DiceKriging
BuildRequires: R-lmtest
BuildRequires: R-sandwich
BuildRequires: R-RcppEigen-devel
BuildRequires: R-mvtnorm
BuildRequires: R-timereg
BuildRequires: R-nloptr
BuildRequires: R-pracma
BuildRequires: R-iterators
BuildRequires: R-zoo
BuildRequires: R-gplots
BuildRequires: R-gtools
BuildRequires: R-caTools
BuildRequires: R-bitops
Suggests: R-DTRlearn2
Suggests: R-glmnet
Suggests: R-mets
Suggests: R-xgboost
Suggests: R-knitr
Suggests: R-ranger
Suggests: R-rmarkdown
Suggests: R-testthat
Suggests: R-ggplot2
%description
Package for learning and evaluating (subgroup) policies via doubly
robust loss functions. Policy learning methods include doubly robust
blip/conditional average treatment effect learning and sequential
policy tree learning. Methods for (subgroup) policy evaluation include
doubly robust cross-fitting and online estimation/sequential
validation. See Nordland and Holst (2022)
<doi:10.48550/arXiv.2212.02335> for documentation and references.
%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
%doc %{rlibdir}/%{packname}/doc
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
%changelog