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math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/api.py
%%PYTHON_SITELIBDIR%%/statsmodels/api.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/api.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/base/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/__init__.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/base/_constraints.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/_constraints.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/_constraints.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/base/_penalties.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/_penalties.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/_penalties.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/base/covtype.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/covtype.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/covtype.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/base/data.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/data.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/data.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/base/l1_cvxopt.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/l1_cvxopt.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/l1_cvxopt.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/base/l1_slsqp.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/l1_slsqp.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/l1_slsqp.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/base/l1_solvers_common.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/l1_solvers_common.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/l1_solvers_common.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/base/model.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/model.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/model.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/base/optimizer.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/optimizer.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/optimizer.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_data.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_data.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_data.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_generic_methods.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_generic_methods.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_generic_methods.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_optimize.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_optimize.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_optimize.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_shrink_pickle.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_shrink_pickle.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/tests/test_shrink_pickle.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/base/wrapper.py
%%PYTHON_SITELIBDIR%%/statsmodels/base/wrapper.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/base/wrapper.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/compat/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/compat/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/compat/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/compat/collections.py
%%PYTHON_SITELIBDIR%%/statsmodels/compat/collections.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/compat/collections.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/compat/counter.py
%%PYTHON_SITELIBDIR%%/statsmodels/compat/counter.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/compat/counter.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/compat/numpy.py
%%PYTHON_SITELIBDIR%%/statsmodels/compat/numpy.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/compat/numpy.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/compat/ordereddict.py
%%PYTHON_SITELIBDIR%%/statsmodels/compat/ordereddict.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/compat/ordereddict.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/compat/python.py
%%PYTHON_SITELIBDIR%%/statsmodels/compat/python.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/compat/python.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/compat/scipy.py
%%PYTHON_SITELIBDIR%%/statsmodels/compat/scipy.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/compat/scipy.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/test_collections.py
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/test_collections.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/test_collections.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/test_itercompat.py
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/test_itercompat.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/test_itercompat.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/test_scipy_compat.py
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/test_scipy_compat.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/compat/tests/test_scipy_compat.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/anes96/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/anes96/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/anes96/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/anes96/anes96.csv
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/anes96/data.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/anes96/data.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/anes96/data.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cancer/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cancer/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cancer/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cancer/cancer.csv
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cancer/data.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cancer/data.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cancer/data.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/ccard/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/ccard/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/ccard/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/ccard/ccard.csv
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/ccard/data.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/ccard/data.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/ccard/data.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/co2/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/co2/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/co2/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/co2/co2.csv
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/co2/data.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/co2/data.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/co2/data.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/committee/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/committee/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/committee/__init__.pyo
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/copper/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cpunish/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cpunish/cpunish.csv
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cpunish/data.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cpunish/data.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/cpunish/data.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/elnino/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/elnino/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/elnino/data.py
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/elnino/elnino.csv
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/engel/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/engel/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/engel/data.py
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/engel/data.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/engel/engel.csv
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/fair/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/fair/fair.csv
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/grunfeld/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/grunfeld/grunfeld.csv
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/heart/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/heart/data.py
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/macrodata/macrodata.csv
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/macrodata/macrodata.dta
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/modechoice/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/modechoice/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/modechoice/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/modechoice/data.py
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/modechoice/data.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/modechoice/data.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/modechoice/modechoice.csv
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/nile/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/datasets/tests/raw.github.com,vincentarelbundock,Rdatasets,master,csv,car,Duncan.csv.zip
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/tests/test_data.py
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math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/datasets/tests/test_utils.py
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/discrete/tests/results/predict_prob_poisson.csv
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math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
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%%PYTHON_SITELIBDIR%%/statsmodels/discrete/tests/results/ships.csv
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/discrete/tests/results/yhat_mnlogit.csv
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/discrete/tests/test_constrained.py
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%%PYTHON_SITELIBDIR%%/statsmodels/discrete/tests/test_constrained.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/discrete/tests/test_discrete.py
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/discrete/tests/test_sandwich_cov.py
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%%PYTHON_SITELIBDIR%%/statsmodels/discrete/tests/test_sandwich_cov.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/distributions/__init__.py
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/distributions/edgeworth.py
%%PYTHON_SITELIBDIR%%/statsmodels/distributions/edgeworth.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/distributions/edgeworth.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/distributions/empirical_distribution.py
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/distributions/tests/test_edgeworth.py
%%PYTHON_SITELIBDIR%%/statsmodels/distributions/tests/test_edgeworth.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/distributions/tests/test_edgeworth.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/distributions/tests/test_mixture.py
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/duration/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/duration/tests/test_phreg.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/duration/tests/test_phreg.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/emplike/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/emplike/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/emplike/aft_el.py
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%%PYTHON_SITELIBDIR%%/statsmodels/emplike/aft_el.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/emplike/api.py
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%%PYTHON_SITELIBDIR%%/statsmodels/emplike/descriptive.py
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%%PYTHON_SITELIBDIR%%/statsmodels/emplike/descriptive.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/emplike/elanova.py
%%PYTHON_SITELIBDIR%%/statsmodels/emplike/elanova.pyc
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%%PYTHON_SITELIBDIR%%/statsmodels/formula/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/formula/api.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/formula/formulatools.py
%%PYTHON_SITELIBDIR%%/statsmodels/formula/formulatools.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/formula/formulatools.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/formula/tests/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/formula/tests/test_formula.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/__init__.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/api.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/api.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/api.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/cov_struct.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/cov_struct.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/cov_struct.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/family.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/family.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/family.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/links.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/links.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/links.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/tests/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/tests/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/tests/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/tests/test_link.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/tests/test_link.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/tests/test_link.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/varfuncs.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/varfuncs.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/families/varfuncs.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/generalized_estimating_equations.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/generalized_estimating_equations.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/generalized_estimating_equations.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/generalized_linear_model.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/generalized_linear_model.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/generalized_linear_model.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/__init__.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_categorical_simulation_check.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_categorical_simulation_check.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_categorical_simulation_check.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_gaussian_simulation_check.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_gaussian_simulation_check.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_gaussian_simulation_check.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_poisson_simulation_check.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_poisson_simulation_check.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_poisson_simulation_check.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_simulation_check.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_simulation_check.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/gee_simulation_check.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/__init__.pyc
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/epil.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/gee_generate_tests.py
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%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/gee_generate_tests.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/gee_linear_1.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/gee_logistic_1.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/gee_nested_linear_1.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/gee_nominal_1.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/gee_ordinal_1.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/gee_poisson_1.csv
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/glm_test_resids.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/glm_test_resids.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/glm_test_resids.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/igaussident_resids.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/inv_gaussian.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/iris.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/medparlogresids.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/results_glm.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/results_glm.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/results_glm.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/stata_cancer_glm.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/stata_lbw_glm.csv
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/results/stata_medpar1_glm.csv
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/test_gee.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/test_gee.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/test_gee.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/test_glm.py
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/test_glm.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/genmod/tests/test_glm.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/api.py
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/api.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/api.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/boxplots.py
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/boxplots.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/boxplots.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/correlation.py
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/correlation.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/correlation.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/dotplots.py
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/dotplots.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/dotplots.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/factorplots.py
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/factorplots.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/factorplots.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/functional.py
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/functional.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/functional.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/gofplots.py
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/gofplots.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/gofplots.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/mosaicplot.py
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/mosaicplot.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/mosaicplot.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/plot_grids.py
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/plot_grids.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/plot_grids.pyo
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/tests/test_dotplot.py
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/tests/test_dotplot.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/graphics/tests/test_dotplot.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/results/results_kde_univ_weights.csv
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/results/results_kde_weights.csv
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/results/results_kernel_regression.csv
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/results/test_lowess_delta.csv
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/test_bandwidths.py
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/test_bandwidths.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/test_bandwidths.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/test_kde.py
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%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/test_kernel_regression.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/test_kernels.py
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/test_kernels.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/test_kernels.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/nonparametric/tests/test_lowess.py
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/mixed_linear_model.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/mixed_linear_model.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/regression/mixed_linear_model.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/quantile_regression.py
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%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/__init__.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/generate_lasso.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/generate_lasso.pyc
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%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/generate_lme.py
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%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/lme_r_results.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/lme_r_results.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/__init__.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/lasso_data.csv
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/leverage_influence_ols_nostars.txt
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/lme00.csv
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/lme01.csv
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%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/lme11.csv
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/macro_gr_corc_stata.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/macro_gr_corc_stata.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/macro_gr_corc_stata.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/results_macro_ols_robust.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/results_macro_ols_robust.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/results_macro_ols_robust.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results/results_regression.py
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%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results_quantile_regression.py
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%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/results_quantile_regression.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_cov.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_cov.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_cov.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_glsar_gretl.py
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%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_glsar_stata.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_glsar_stata.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_lme.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_lme.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_lme.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_quantile_regression.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_quantile_regression.pyc
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%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_regression.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_regression.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_regression.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_robustcov.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/test_robustcov.pyc
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%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/tests_predict.py
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/tests_predict.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/regression/tests/tests_predict.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/resampling/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/robust/robust_linear_model.py
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%%PYTHON_SITELIBDIR%%/statsmodels/robust/scale.py
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%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/bspline.py
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%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/contrast_old.py
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%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/copula.py
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%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/estimators.py
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%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/examples/__init__.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/examples/ex_extras.py
%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/examples/ex_extras.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/examples/ex_extras.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/examples/ex_fitfr.py
%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/examples/ex_fitfr.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/examples/ex_fitfr.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/examples/ex_gof.py
%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/examples/ex_gof.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/sandbox/distributions/examples/ex_gof.pyo
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
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math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
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math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
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math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
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%%PYTHON_SITELIBDIR%%/statsmodels/tools/print_version.py
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%%PYTHON_SITELIBDIR%%/statsmodels/tools/rootfinding.py
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tools/testing.py
%%PYTHON_SITELIBDIR%%/statsmodels/tools/testing.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tools/testing.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/__init__.py
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_grouputils.py
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_grouputils.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_grouputils.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_numdiff.py
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_numdiff.pyc
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%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_rootfinding.py
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%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_tools.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_tools.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_transform_model.py
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_transform_model.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_transform_model.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_web.py
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_web.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tests/test_web.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tools.py
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tools.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tools/tools.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tools/transform_model.py
%%PYTHON_SITELIBDIR%%/statsmodels/tools/transform_model.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tools/transform_model.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tools/web.py
%%PYTHON_SITELIBDIR%%/statsmodels/tools/web.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tools/web.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tools/wrappers.py
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%%PYTHON_SITELIBDIR%%/statsmodels/tsa/base/tests/test_datetools.py
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%%PYTHON_SITELIBDIR%%/statsmodels/tsa/base/tsa_model.py
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%%PYTHON_SITELIBDIR%%/statsmodels/tsa/base/tsa_model.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/descriptivestats.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/descriptivestats.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/descriptivestats.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/__init__.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/_utils.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/_utils.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/_utils.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/api.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/api.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/api.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/bk_filter.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/bk_filter.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/bk_filter.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/cf_filter.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/cf_filter.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/cf_filter.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/filtertools.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/filtertools.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/filtertools.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/hp_filter.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/hp_filter.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/hp_filter.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/tests/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/tests/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/tests/__init__.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/tests/results/__init__.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/tests/results/__init__.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/tests/results/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/tests/results/filter_results.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/tests/results/filter_results.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/tests/results/filter_results.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/tests/test_filters.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/filters/tests/test_filters.pyc
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%%PYTHON_SITELIBDIR%%/statsmodels/tsa/interp/__init__.pyo
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%%PYTHON_SITELIBDIR%%/statsmodels/tsa/interp/denton.pyo
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%%PYTHON_SITELIBDIR%%/statsmodels/tsa/interp/tests/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/interp/tests/test_denton.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/interp/tests/test_denton.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/interp/tests/test_denton.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/kalmanf/__init__.py
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%%PYTHON_SITELIBDIR%%/statsmodels/tsa/kalmanf/__init__.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/kalmanf/kalman_loglike.so
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/kalmanf/kalmanfilter.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/kalmanf/kalmanfilter.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/kalmanf/kalmanfilter.pyo
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/mlemodel.py
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/mlemodel.pyc
%%PYTHON_SITELIBDIR%%/statsmodels/tsa/mlemodel.pyo
math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
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math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
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math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
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math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
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%%PYTHON_SITELIBDIR%%/statsmodels/tsa/vector_ar/tests/results/results_svar_st.pyo
math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
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math/py-statsmodels: Complement to SciPy for statistical computations Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: * linear regression models: GLS (including WLS and LS aith AR errors) and OLS. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: VAR and structural VAR * nonparametric: (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests, diagnostics and specification tests * iolib: Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of * developement and testing which is not considered "production ready", including Mixed models, GARCH and GMM estimators, kernel regression, panel data models. WWW: https://www.github.com/statsmodels/statsmodels PR: ports/183932 Submitted by: Johannes Jost Meixner <xmj chaot.net>
2013-11-22 13:40:09 +01:00
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math/py-statsmodels: update to 0.6.1, unbreak, modernize - update to 0.6.1 and unbreak - replace noegginfo with concurrent in USE_PYTHON Differential Revision: https://reviews.freebsd.org/D1287 Approved by: swills (mentor)
2014-12-09 18:33:53 +01:00
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%%PORTDOCS%%%%DOCSDIR%%/generated/statsmodels.stats.outliers_influence.OLSInfluence.dffits_internal.html
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%%PORTDOCS%%%%DOCSDIR%%/generated/statsmodels.stats.outliers_influence.OLSInfluence.influence.html
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%%PORTDOCS%%%%DOCSDIR%%/generated/statsmodels.tools.eval_measures.stde.html
%%PORTDOCS%%%%DOCSDIR%%/generated/statsmodels.tools.eval_measures.vare.html
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%%PORTDOCS%%%%DOCSDIR%%/generated/statsmodels.tsa.arima_process.ArmaProcess.arma2ar.html
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