File R-konfound.spec of Package R-konfound

# Automatically generated by CRAN2OBS
# 
# Spec file for package konfound 
# 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  konfound 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        1.0.3 
Release:        0 
Summary:        Quantify the Robustness of Causal Inferences 
Group:          Development/Libraries/Other 
License:        MIT + file LICENSE 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         konfound_1.0.3.tar.gz 
Requires:       R-base 
Requires:	R-broom
Requires:	R-broom.mixed
Requires:	R-crayon
Requires:	R-dplyr
Requires:	R-ggplot2
Requires:	R-lavaan
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Requires:	R-stringi
Requires:	R-digest
Requires:	R-listenv
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Requires:	R-rbibutils
 
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Suggests:	R-covr
Suggests:	R-devtools
Suggests:	R-forcats
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Suggests:	R-rmarkdown
Suggests:	R-mice
Suggests:	R-roxygen2
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%description 
Statistical methods that quantify the conditions necessary to alter 
inferences, also known as sensitivity analysis, are becoming 
increasingly important to a variety of quantitative sciences. A series 
of recent works, including Frank (2000) 
<doi:10.1177/0049124100029002001> and Frank et al. (2013) 
<doi:10.3102/0162373713493129> extend previous sensitivity analyses by 
considering the characteristics of omitted variables or unobserved 
cases that would change an inference if such variables or cases were 
observed. These analyses generate statements such as "an omitted 
variable would have to be correlated at xx with the predictor of 
interest (e.g., the treatment) and outcome to invalidate an inference 
of a treatment effect". Or "one would have to replace pp percent of the 
observed data with nor which the treatment had no effect to invalidate 
the inference". We implement these recent developments of sensitivity 
analysis and provide modules to calculate these two robustness indices 
and generate such statements in R. In particular, the functions 
konfound(), pkonfound() and mkonfound() allow users to calculate the 
robustness of inferences for a user's own model, a single published 
study and multiple studies respectively. 
 
%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
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/data
%doc %{rlibdir}/%{packname}/doc
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog