File R-generalCorr.spec of Package R-generalCorr

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# Spec file for package generalCorr 
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%global packname  generalCorr 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        1.2.6 
Release:        0 
Summary:        Generalized Correlations, Causal Paths and Portfolio Selection 
Group:          Development/Libraries/Other 
License:        GPL (>= 2) 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         generalCorr_1.2.6.tar.gz 
Requires:       R-base 
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Suggests:	R-R.rsp
%description 
Function gmcmtx0() computes a more reliable (general) correlation 
matrix. Since causal paths from data are important for all sciences, 
the package provides many sophisticated functions. causeSummBlk() and 
causeSum2Blk() give easy-to-interpret causal paths.  Let Z denote 
control variables and compare two flipped kernel regressions: X=f(Y, 
Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| 
then variation in X is more "exogenous or independent" than in Y, and 
the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These 
inequalities between many absolute values are quantified by four orders 
of stochastic dominance. Our third criterion Cr3, for the causal path X 
to Y, requires new generalized partial correlations to satisfy 
|r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized 
partials between the first variable and all others.  The package 
provides several R functions including get0outliers() for outlier 
detection, bigfp() for numerical integration by the trapezoidal rule, 
stochdom2() for stochastic dominance, pillar3D() for 3D charts, 
canonRho() for generalized canonical correlations, depMeas() measures 
nonlinear dependence, and causeSummary(mtx) reports summary of causal 
paths among matrix columns. Portfolio selection: decileVote(), 
momentVote(), dif4mtx(), exactSdMtx() can rank several stocks. 
Functions whose names begin with 'boot' provide bootstrap statistical 
inference, including a new bootGcRsq() test for "Granger-causality" 
allowing nonlinear relations. A new tool for evaluation of 
out-of-sample portfolio performance is outOFsamp(). Panel data 
implementation is now included. See eight vignettes of the package for 
theory, examples, and usage tips. See Vinod (2019) 
\doi{10.1080/03610918.2015.1122048}. 
 
%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
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/data
%{rlibdir}/%{packname}/doc
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog