automatic import of python-correlationopeneuler20.03

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+/correlation-1.0.0.tar.gz
diff --git a/python-correlation.spec b/python-correlation.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-correlation
+Version:	1.0.0
+Release:	1
+Summary:	Calculate the confidence intervals of correlation coeficients
+License:	BSD 2-Clause License
+URL:		https://github.com/XiangwenWang/correlation
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/30/b9/f4fcac90062b340c0fa5d015979818f6d9c61d37393163dfbbe369ad4d70/correlation-1.0.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+
+%description
+# correlation
+
+Calculate confidence intervals for correlation coefficients, including Pearson's R, Kendall's tau, Spearman's rho, and customized correlation measures.
+
+## Methodology  
+Two approaches are offered to calculate the confidence intervals, one parametric approach based on normal approximation, and one non-parametric approach based on bootstrapping.
+### Parametric Approach
+Say r\_hat is the correlation we obtained, then with a transformation  
+```
+z = ln((1+r)/(1-r))/2,
+```  
+z would approximately follow a normal distribution,  
+with a mean equals to z(r\_hat),  
+and a variance sigma^2 that equals to 1/(n-3), 0.437/(n-4), (1+r_hat^2/2)/(n-3) for the Pearson's r, Kendall's tau, and Spearman's rho, respectively (read Ref. [1, 2] for more details). n is the array length.
+
+The (1-alpha) CI for r would be  
+```
+(T(z_lower), T(z_upper))
+```  
+where T is the inverse of the transformation mentioned earlier  
+```
+T(x) = (exp(2x) - 1) / (exp(2x) + 1),
+```   
+```
+z_lower = z - z_(1-alpha/2) sigma,
+```  
+```
+z_upper = z + z_(1-alpha/2) sigma.
+```
+
+This normal approximation works when the absolute values of the Pearson's r, Kendall's tau, and Spearman's rho are less than 1, 0.8, and 0.95, respectively.
+
+### Nonparametric Approach
+For the nonparametric approach, we simply adopt a naive bootstrap method.
+
+* We sample a pair (x\_i, y\_i) with replacement from the original (paired) samples until we have a sample size that equals to n, and calculate a correlation coefficient from the new samples.  
+* Repeat this process for a large number of times (by default we use 5000),
+* then we could obtain the (1-alpha) CI for r by taking the alpha/2 and (1-alpha/2) quantiles of the obtained correlation coefficients.
+
+
+## References
+[1] Bonett, Douglas G., and Thomas A. Wright. "Sample size requirements for estimating Pearson, Kendall and Spearman correlations." Psychometrika 65, no. 1 (2000): 23-28.  
+[2] Bishara, Anthony J., and James B. Hittner. "Confidence intervals for correlations when data are not normal." Behavior research methods 49, no. 1 (2017): 294-309.
+
+
+## Installation:  
+```
+pip install correlation
+```  
+or
+
+```
+conda install -c wangxiangwen correlation
+```
+
+## Example Usage:  
+```python
+>>> import correlation
+>>> a, b = list(range(2000)), list(range(200, 0, -1)) * 10
+>>> correlation.corr(a, b, method='spearman_rho')
+(-0.0999987624920335,          # correlation coefficient
+ -0.14330929583811683,         # lower endpoint of CI
+ -0.056305939127336606,        # upper endpoint of CI
+ 7.446171861744971e-06)        # p-value
+```
+
+
+
+
+%package -n python3-correlation
+Summary:	Calculate the confidence intervals of correlation coeficients
+Provides:	python-correlation
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-correlation
+# correlation
+
+Calculate confidence intervals for correlation coefficients, including Pearson's R, Kendall's tau, Spearman's rho, and customized correlation measures.
+
+## Methodology  
+Two approaches are offered to calculate the confidence intervals, one parametric approach based on normal approximation, and one non-parametric approach based on bootstrapping.
+### Parametric Approach
+Say r\_hat is the correlation we obtained, then with a transformation  
+```
+z = ln((1+r)/(1-r))/2,
+```  
+z would approximately follow a normal distribution,  
+with a mean equals to z(r\_hat),  
+and a variance sigma^2 that equals to 1/(n-3), 0.437/(n-4), (1+r_hat^2/2)/(n-3) for the Pearson's r, Kendall's tau, and Spearman's rho, respectively (read Ref. [1, 2] for more details). n is the array length.
+
+The (1-alpha) CI for r would be  
+```
+(T(z_lower), T(z_upper))
+```  
+where T is the inverse of the transformation mentioned earlier  
+```
+T(x) = (exp(2x) - 1) / (exp(2x) + 1),
+```   
+```
+z_lower = z - z_(1-alpha/2) sigma,
+```  
+```
+z_upper = z + z_(1-alpha/2) sigma.
+```
+
+This normal approximation works when the absolute values of the Pearson's r, Kendall's tau, and Spearman's rho are less than 1, 0.8, and 0.95, respectively.
+
+### Nonparametric Approach
+For the nonparametric approach, we simply adopt a naive bootstrap method.
+
+* We sample a pair (x\_i, y\_i) with replacement from the original (paired) samples until we have a sample size that equals to n, and calculate a correlation coefficient from the new samples.  
+* Repeat this process for a large number of times (by default we use 5000),
+* then we could obtain the (1-alpha) CI for r by taking the alpha/2 and (1-alpha/2) quantiles of the obtained correlation coefficients.
+
+
+## References
+[1] Bonett, Douglas G., and Thomas A. Wright. "Sample size requirements for estimating Pearson, Kendall and Spearman correlations." Psychometrika 65, no. 1 (2000): 23-28.  
+[2] Bishara, Anthony J., and James B. Hittner. "Confidence intervals for correlations when data are not normal." Behavior research methods 49, no. 1 (2017): 294-309.
+
+
+## Installation:  
+```
+pip install correlation
+```  
+or
+
+```
+conda install -c wangxiangwen correlation
+```
+
+## Example Usage:  
+```python
+>>> import correlation
+>>> a, b = list(range(2000)), list(range(200, 0, -1)) * 10
+>>> correlation.corr(a, b, method='spearman_rho')
+(-0.0999987624920335,          # correlation coefficient
+ -0.14330929583811683,         # lower endpoint of CI
+ -0.056305939127336606,        # upper endpoint of CI
+ 7.446171861744971e-06)        # p-value
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for correlation
+Provides:	python3-correlation-doc
+%description help
+# correlation
+
+Calculate confidence intervals for correlation coefficients, including Pearson's R, Kendall's tau, Spearman's rho, and customized correlation measures.
+
+## Methodology  
+Two approaches are offered to calculate the confidence intervals, one parametric approach based on normal approximation, and one non-parametric approach based on bootstrapping.
+### Parametric Approach
+Say r\_hat is the correlation we obtained, then with a transformation  
+```
+z = ln((1+r)/(1-r))/2,
+```  
+z would approximately follow a normal distribution,  
+with a mean equals to z(r\_hat),  
+and a variance sigma^2 that equals to 1/(n-3), 0.437/(n-4), (1+r_hat^2/2)/(n-3) for the Pearson's r, Kendall's tau, and Spearman's rho, respectively (read Ref. [1, 2] for more details). n is the array length.
+
+The (1-alpha) CI for r would be  
+```
+(T(z_lower), T(z_upper))
+```  
+where T is the inverse of the transformation mentioned earlier  
+```
+T(x) = (exp(2x) - 1) / (exp(2x) + 1),
+```   
+```
+z_lower = z - z_(1-alpha/2) sigma,
+```  
+```
+z_upper = z + z_(1-alpha/2) sigma.
+```
+
+This normal approximation works when the absolute values of the Pearson's r, Kendall's tau, and Spearman's rho are less than 1, 0.8, and 0.95, respectively.
+
+### Nonparametric Approach
+For the nonparametric approach, we simply adopt a naive bootstrap method.
+
+* We sample a pair (x\_i, y\_i) with replacement from the original (paired) samples until we have a sample size that equals to n, and calculate a correlation coefficient from the new samples.  
+* Repeat this process for a large number of times (by default we use 5000),
+* then we could obtain the (1-alpha) CI for r by taking the alpha/2 and (1-alpha/2) quantiles of the obtained correlation coefficients.
+
+
+## References
+[1] Bonett, Douglas G., and Thomas A. Wright. "Sample size requirements for estimating Pearson, Kendall and Spearman correlations." Psychometrika 65, no. 1 (2000): 23-28.  
+[2] Bishara, Anthony J., and James B. Hittner. "Confidence intervals for correlations when data are not normal." Behavior research methods 49, no. 1 (2017): 294-309.
+
+
+## Installation:  
+```
+pip install correlation
+```  
+or
+
+```
+conda install -c wangxiangwen correlation
+```
+
+## Example Usage:  
+```python
+>>> import correlation
+>>> a, b = list(range(2000)), list(range(200, 0, -1)) * 10
+>>> correlation.corr(a, b, method='spearman_rho')
+(-0.0999987624920335,          # correlation coefficient
+ -0.14330929583811683,         # lower endpoint of CI
+ -0.056305939127336606,        # upper endpoint of CI
+ 7.446171861744971e-06)        # p-value
+```
+
+
+
+
+%prep
+%autosetup -n correlation-1.0.0
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-correlation -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
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@@ -0,0 +1 @@
+6cac8df51c28be50e1cfeb964c5c122c  correlation-1.0.0.tar.gz