89 lines
3.4 KiB
RPMSpec
89 lines
3.4 KiB
RPMSpec
#
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# spec file for package perl-Statistics-LineFit
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#
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# Copyright (c) 2024 SUSE LLC
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#
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# All modifications and additions to the file contributed by third parties
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# remain the property of their copyright owners, unless otherwise agreed
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# upon. The license for this file, and modifications and additions to the
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# file, is the same license as for the pristine package itself (unless the
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# license for the pristine package is not an Open Source License, in which
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# case the license is the MIT License). An "Open Source License" is a
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# license that conforms to the Open Source Definition (Version 1.9)
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# published by the Open Source Initiative.
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# Please submit bugfixes or comments via https://bugs.opensuse.org/
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#
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%define cpan_name Statistics-LineFit
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Name: perl-Statistics-LineFit
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Version: 0.70.0
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Release: 0
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# 0.07 -> normalize -> 0.70.0
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%define cpan_version 0.07
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License: Artistic-1.0 OR GPL-1.0-or-later
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Summary: Least squares line fit, weighted or unweighted
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URL: https://metacpan.org/release/%{cpan_name}
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Source0: https://cpan.metacpan.org/authors/id/R/RA/RANDERSON/%{cpan_name}-%{cpan_version}.tar.gz
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Source100: README.md
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BuildArch: noarch
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BuildRequires: perl
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BuildRequires: perl-macros
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Provides: perl(Statistics::LineFit) = 0.60.0
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%undefine __perllib_provides
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%{perl_requires}
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%description
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The Statistics::LineFit module does weighted or unweighted least-squares
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line fitting to two-dimensional data (y = a + b * x). (This is also called
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linear regression.) In addition to the slope and y-intercept, the module
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can return the square of the correlation coefficient (R squared), the
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Durbin-Watson statistic, the mean squared error, sigma, the t statistics,
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the variance of the estimates of the slope and y-intercept, the predicted y
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values and the residuals of the y values. (See the METHODS section for a
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description of these statistics.)
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The module accepts input data in separate x and y arrays or a single 2-D
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array (an array of arrayrefs). The optional weights are input in a separate
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array. The module can optionally verify that the input data and weights are
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valid numbers. If weights are input, the line fit minimizes the weighted
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sum of the squared errors and the following statistics are weighted: the
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correlation coefficient, the Durbin-Watson statistic, the mean squared
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error, sigma and the t statistics.
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The module is state-oriented and caches its results. Once you call the
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setData() method, you can call the other methods in any order or call a
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method several times without invoking redundant calculations. After calling
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setData(), you can modify the input data or weights without affecting the
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module's results.
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The decision to use or not use weighting could be made using your a priori
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knowledge of the data or using supplemental data. If the data is sparse or
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contains non-random noise, weighting can degrade the solution. Weighting is
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a good option if some points are suspect or less relevant (e.g., older
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terms in a time series, points that are known to have more noise).
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%prep
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%autosetup -n %{cpan_name}
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find . -type f ! -path "*/t/*" ! -name "*.pl" ! -path "*/bin/*" ! -path "*/script/*" ! -path "*/scripts/*" ! -name "configure" -print0 | xargs -0 chmod 644
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%build
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perl Makefile.PL INSTALLDIRS=vendor
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%make_build
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%check
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make test
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%install
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%perl_make_install
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%perl_process_packlist
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%perl_gen_filelist
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%files -f %{name}.files
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%doc Changes README Todo
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%license LICENSE
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%changelog
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