Overview

File R-cramR.spec of Package R-cramR

# Automatically generated by CRAN2OBS
# 
# Spec file for package cramR 
# This file is auto-generated using information in the package source, 
# esp. Description and Summary. Improvements in that area should be 
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%global packname  cramR 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        0.1.1 
Release:        0 
Summary:        Cram Method for Efficient Simultaneous Learning and Evaluation 
Group:          Development/Libraries/Other 
License:        GPL-3 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         cramR_0.1.1.tar.gz 
Requires:       R-base 
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Suggests:	R-testthat
Suggests:	R-covr
Suggests:	R-kableExtra
Suggests:	R-profvis
Suggests:	R-devtools
Suggests:	R-waldo
Suggests:	R-knitr
Suggests:	R-rmarkdown
Suggests:	R-randomForest
Suggests:	R-gbm
Suggests:	R-withr
%description 
Performs the Cram method, a general and efficient approach to 
simultaneous learning and evaluation using a generic machine learning 
algorithm. In a single pass of batched data, the proposed method 
repeatedly trains a machine learning algorithm and tests its empirical 
performance. Because it utilizes the entire sample for both learning 
and evaluation, cramming is significantly more data-efficient than 
sample-splitting. Unlike cross-validation, Cram evaluates the final 
learned model directly, providing sharper inference aligned with 
real-world deployment. The method naturally applies to both policy 
learning and contextual bandits, where decisions are based on 
individual features to maximize outcomes. The package includes 
cram_policy() for learning and evaluating individualized binary 
treatment rules, cram_ml() to train and assess the population-level 
performance of machine learning models, and cram_bandit() for on-policy 
evaluation of contextual bandit algorithms. For all three functions, 
the package provides estimates of the average outcome that would result 
if the model were deployed, along with standard errors and confidence 
intervals for these estimates. Details of the method are described in 
Jia, Imai, and Li (2024) 
<https://www.hbs.edu/ris/Publication%20Files/2403.07031v1_a83462e0-145b-4675-99d5-9754aa65d786.pdf> 
and Jia et al. (2025) <doi:10.48550/arXiv.2403.07031>. 
 
%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}/doc
%{rlibdir}/%{packname}/examples
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog
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