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File R-csmpv.spec of Package R-csmpv

# Automatically generated by CRAN2OBS
# 
# Spec file for package csmpv 
# 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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%global packname  csmpv 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        1.0.3 
Release:        0 
Summary:        Biomarker Confirmation, Selection, Modelling, Prediction, and Validation 
Group:          Development/Libraries/Other 
License:        MIT + file LICENSE 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         csmpv_1.0.3.tar.gz 
Requires:       R-base 
Requires:	R-glmnet
Requires:	R-Hmisc
Requires:	R-rms
Requires:	R-forestmodel
Requires:	R-ggplot2
Requires:	R-ggpubr
Requires:	R-survminer
Requires:	R-xgboost
Requires:	R-scales
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Suggests:	R-knitr
Suggests:	R-rmarkdown
Suggests:	R-devtools
%description 
There are diverse purposes such as biomarker confirmation, novel 
biomarker discovery, constructing predictive models, model-based 
prediction, and validation. It handles binary, continuous, and 
time-to-event outcomes at the sample or patient level. - Biomarker 
confirmation utilizes established functions like glm() from 'stats', 
coxph() from 'survival', surv_fit(), and ggsurvplot() from 'survminer'. 
- Biomarker discovery and variable selection are facilitated by three 
LASSO-related functions LASSO2(), LASSO_plus(), and LASSO2plus(), 
leveraging the 'glmnet' R package with additional steps. - Eight 
versatile modeling functions are offered, each designed for predictive 
models across various outcomes and data types. 1) LASSO2(), 
LASSO_plus(), LASSO2plus(), and LASSO2_reg() perform variable selection 
using LASSO methods and construct predictive models based on selected 
variables. 2) XGBtraining() employs 'XGBoost' for model building and is 
the only function not involving variable selection. 3) Functions like 
LASSO2_XGBtraining(), LASSOplus_XGBtraining(), and 
LASSO2plus_XGBtraining() combine LASSO-related variable selection with 
'XGBoost' for model construction. - All models support prediction and 
validation, requiring a testing dataset comparable to the training 
dataset. Additionally, the package introduces XGpred() for risk 
prediction based on survival data, with the XGpred_predict() function 
available for predicting risk groups in new datasets. The methodology 
is based on our new algorithms and various references: - Hastie et al. 
(1992, ISBN 0 534 16765-9), - Therneau et al. (2000, ISBN 
0-387-98784-3), - Kassambara et al. (2021) 
<https://CRAN.R-project.org/package=survminer>, - Friedman et al. 
(2010) <doi:10.18637/jss.v033.i01>, - Simon et al. (2011) 
<doi:10.18637/jss.v039.i05>, - Harrell (2023) 
<https://CRAN.R-project.org/package=rms>, - Harrell (2023) 
<https://CRAN.R-project.org/package=Hmisc>, - Chen and Guestrin (2016) 
<arXiv:1603.02754>, - Aoki et al. (2023) <doi:10.1200/JCO.23.01115>. 
 
%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
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/data
%{rlibdir}/%{packname}/doc
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog
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