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| author | CoprDistGit <infra@openeuler.org> | 2023-04-10 18:46:53 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-04-10 18:46:53 +0000 |
| commit | 7d97b0fa58985bc7ee1a48ca4de2f4c2ff10d73c (patch) | |
| tree | b97093531e8d693d86b06ab62e2c22f1a7cb22b1 | |
| parent | af1625b599b1deb957abe1f83b6ac5e79495917d (diff) | |
automatic import of python-linearmodels
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-linearmodels.spec | 567 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 569 insertions, 0 deletions
@@ -0,0 +1 @@ +/linearmodels-4.27.tar.gz diff --git a/python-linearmodels.spec b/python-linearmodels.spec new file mode 100644 index 0000000..9534c9b --- /dev/null +++ b/python-linearmodels.spec @@ -0,0 +1,567 @@ +%global _empty_manifest_terminate_build 0 +Name: python-linearmodels +Version: 4.27 +Release: 1 +Summary: Linear Panel, Instrumental Variable, Asset Pricing, and System Regression models for Python +License: NCSA +URL: http://github.com/bashtage/linearmodels +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ab/db/dcd2f13a06cb7537b65464e3632f731ff6f4db344e0b9d6c8d8e3d95fe62/linearmodels-4.27.tar.gz + +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-scipy +Requires: python3-statsmodels +Requires: python3-property-cached +Requires: python3-mypy-extensions +Requires: python3-Cython +Requires: python3-pyhdfe +Requires: python3-formulaic +Requires: python3-setuptools-scm + +%description +# Linear Models + +| Metric | | +| :------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **Latest Release** | [](https://badge.fury.io/py/linearmodels) | +| **Continuous Integration** | [](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) | +| | [](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) | +| **Coverage** | [](https://codecov.io/gh/bashtage/linearmodels) | +| **Code Quality** | [](https://www.codacy.com/manual/bashtage/linearmodels?utm_source=github.com&utm_medium=referral&utm_content=bashtage/linearmodels&utm_campaign=Badge_Grade) | +| | [](https://codebeat.co/projects/github-com-bashtage-linearmodels-main) | +| | [](https://lgtm.com/projects/g/bashtage/linearmodels/context:python) | +| | [](https://lgtm.com/projects/g/bashtage/linearmodels/alerts) | +| **Citation** | [](https://zenodo.org/badge/latestdoi/82291672) | + +Linear (regression) models for Python. Extends +[statsmodels](http://www.statsmodels.org) with Panel regression, +instrumental variable estimators, system estimators and models for +estimating asset prices: + +- **Panel models**: + - Fixed effects (maximum two-way) + - First difference regression + - Between estimator for panel data + - Pooled regression for panel data + - Fama-MacBeth estimation of panel models + +- **High-dimensional Regresssion**: + - Absorbing Least Squares + +- **Instrumental Variable estimators** + - Two-stage Least Squares + - Limited Information Maximum Likelihood + - k-class Estimators + - Generalized Method of Moments, also with continuously updating + +- **Factor Asset Pricing Models**: + - 2- and 3-step estimation + - Time-series estimation + - GMM estimation + +- **System Regression**: + - Seemingly Unrelated Regression (SUR/SURE) + - Three-Stage Least Squares (3SLS) + - Generalized Method of Moments (GMM) System Estimation + +Designed to work equally well with NumPy, Pandas or xarray data. + +## Panel models + +Like [statsmodels](http://www.statsmodels.org) to include, supports +formulas for specifying models. For example, the classic Grunfeld regression can be +specified + +```python +import numpy as np +from statsmodels.datasets import grunfeld +data = grunfeld.load_pandas().data +data.year = data.year.astype(np.int64) +# MultiIndex, entity - time +data = data.set_index(['firm','year']) +from linearmodels import PanelOLS +mod = PanelOLS(data.invest, data[['value','capital']], entity_effects=True) +res = mod.fit(cov_type='clustered', cluster_entity=True) +``` + +Models can also be specified using the formula interface. + +```python +from linearmodels import PanelOLS +mod = PanelOLS.from_formula('invest ~ value + capital + EntityEffects', data) +res = mod.fit(cov_type='clustered', cluster_entity=True) +``` + +The formula interface for `PanelOLS` supports the special values +`EntityEffects` and `TimeEffects` which add entity (fixed) and time +effects, respectively. + +Formula support comes from the [formulaic](https://github.com/matthewwardrop/formulaic/) +package which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/). + +## Instrumental Variable Models + +IV regression models can be similarly specified. + +```python +import numpy as np +from linearmodels.iv import IV2SLS +from linearmodels.datasets import mroz +data = mroz.load() +mod = IV2SLS.from_formula('np.log(wage) ~ 1 + exper + exper ** 2 + [educ ~ motheduc + fatheduc]', data) +``` + +The expressions in the `[ ]` indicate endogenous regressors (before `~`) +and the instruments. + +## Installing + +The latest release can be installed using pip + +```bash +pip install linearmodels +``` + +The main branch can be installed by cloning the repo and running setup + +```bash +git clone https://github.com/bashtage/linearmodels +cd linearmodels +pip install . +``` + +## Documentation + +[Stable Documentation](https://bashtage.github.io/linearmodels/) is +built on every tagged version using +[doctr](https://github.com/drdoctr/doctr). +[Development Documentation](https://bashtage.github.io/linearmodels/devel) +is automatically built on every successful build of main. + +## Plan and status + +Should eventually add some useful linear model estimators such as panel +regression. Currently only the single variable IV estimators are polished. + +- Linear Instrumental variable estimation - **complete** +- Linear Panel model estimation - **complete** +- Fama-MacBeth regression - **complete** +- Linear Factor Asset Pricing - **complete** +- System regression - **complete** +- Linear IV Panel model estimation - _not started_ +- Dynamic Panel model estimation - _not started_ + +## Requirements + +### Running + +With the exception of Python 3 (3.8+ tested), which is a hard requirement, the +others are the version that are being used in the test environment. It +is possible that older versions work. + +- Python 3.8+ +- NumPy (1.18+) +- SciPy (1.3+) +- pandas (1.0+) +- statsmodels (0.12+) +- xarray (0.16+, optional) +- Cython (0.29.24+, optional) + +### Testing + +- py.test + +### Documentation + +- sphinx +- sphinx-material +- nbsphinx +- nbconvert +- nbformat +- ipython +- jupyter + + +%package -n python3-linearmodels +Summary: Linear Panel, Instrumental Variable, Asset Pricing, and System Regression models for Python +Provides: python-linearmodels +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +BuildRequires: python3-cffi +BuildRequires: gcc +BuildRequires: gdb +%description -n python3-linearmodels +# Linear Models + +| Metric | | +| :------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **Latest Release** | [](https://badge.fury.io/py/linearmodels) | +| **Continuous Integration** | [](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) | +| | [](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) | +| **Coverage** | [](https://codecov.io/gh/bashtage/linearmodels) | +| **Code Quality** | [](https://www.codacy.com/manual/bashtage/linearmodels?utm_source=github.com&utm_medium=referral&utm_content=bashtage/linearmodels&utm_campaign=Badge_Grade) | +| | [](https://codebeat.co/projects/github-com-bashtage-linearmodels-main) | +| | [](https://lgtm.com/projects/g/bashtage/linearmodels/context:python) | +| | [](https://lgtm.com/projects/g/bashtage/linearmodels/alerts) | +| **Citation** | [](https://zenodo.org/badge/latestdoi/82291672) | + +Linear (regression) models for Python. Extends +[statsmodels](http://www.statsmodels.org) with Panel regression, +instrumental variable estimators, system estimators and models for +estimating asset prices: + +- **Panel models**: + - Fixed effects (maximum two-way) + - First difference regression + - Between estimator for panel data + - Pooled regression for panel data + - Fama-MacBeth estimation of panel models + +- **High-dimensional Regresssion**: + - Absorbing Least Squares + +- **Instrumental Variable estimators** + - Two-stage Least Squares + - Limited Information Maximum Likelihood + - k-class Estimators + - Generalized Method of Moments, also with continuously updating + +- **Factor Asset Pricing Models**: + - 2- and 3-step estimation + - Time-series estimation + - GMM estimation + +- **System Regression**: + - Seemingly Unrelated Regression (SUR/SURE) + - Three-Stage Least Squares (3SLS) + - Generalized Method of Moments (GMM) System Estimation + +Designed to work equally well with NumPy, Pandas or xarray data. + +## Panel models + +Like [statsmodels](http://www.statsmodels.org) to include, supports +formulas for specifying models. For example, the classic Grunfeld regression can be +specified + +```python +import numpy as np +from statsmodels.datasets import grunfeld +data = grunfeld.load_pandas().data +data.year = data.year.astype(np.int64) +# MultiIndex, entity - time +data = data.set_index(['firm','year']) +from linearmodels import PanelOLS +mod = PanelOLS(data.invest, data[['value','capital']], entity_effects=True) +res = mod.fit(cov_type='clustered', cluster_entity=True) +``` + +Models can also be specified using the formula interface. + +```python +from linearmodels import PanelOLS +mod = PanelOLS.from_formula('invest ~ value + capital + EntityEffects', data) +res = mod.fit(cov_type='clustered', cluster_entity=True) +``` + +The formula interface for `PanelOLS` supports the special values +`EntityEffects` and `TimeEffects` which add entity (fixed) and time +effects, respectively. + +Formula support comes from the [formulaic](https://github.com/matthewwardrop/formulaic/) +package which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/). + +## Instrumental Variable Models + +IV regression models can be similarly specified. + +```python +import numpy as np +from linearmodels.iv import IV2SLS +from linearmodels.datasets import mroz +data = mroz.load() +mod = IV2SLS.from_formula('np.log(wage) ~ 1 + exper + exper ** 2 + [educ ~ motheduc + fatheduc]', data) +``` + +The expressions in the `[ ]` indicate endogenous regressors (before `~`) +and the instruments. + +## Installing + +The latest release can be installed using pip + +```bash +pip install linearmodels +``` + +The main branch can be installed by cloning the repo and running setup + +```bash +git clone https://github.com/bashtage/linearmodels +cd linearmodels +pip install . +``` + +## Documentation + +[Stable Documentation](https://bashtage.github.io/linearmodels/) is +built on every tagged version using +[doctr](https://github.com/drdoctr/doctr). +[Development Documentation](https://bashtage.github.io/linearmodels/devel) +is automatically built on every successful build of main. + +## Plan and status + +Should eventually add some useful linear model estimators such as panel +regression. Currently only the single variable IV estimators are polished. + +- Linear Instrumental variable estimation - **complete** +- Linear Panel model estimation - **complete** +- Fama-MacBeth regression - **complete** +- Linear Factor Asset Pricing - **complete** +- System regression - **complete** +- Linear IV Panel model estimation - _not started_ +- Dynamic Panel model estimation - _not started_ + +## Requirements + +### Running + +With the exception of Python 3 (3.8+ tested), which is a hard requirement, the +others are the version that are being used in the test environment. It +is possible that older versions work. + +- Python 3.8+ +- NumPy (1.18+) +- SciPy (1.3+) +- pandas (1.0+) +- statsmodels (0.12+) +- xarray (0.16+, optional) +- Cython (0.29.24+, optional) + +### Testing + +- py.test + +### Documentation + +- sphinx +- sphinx-material +- nbsphinx +- nbconvert +- nbformat +- ipython +- jupyter + + +%package help +Summary: Development documents and examples for linearmodels +Provides: python3-linearmodels-doc +%description help +# Linear Models + +| Metric | | +| :------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **Latest Release** | [](https://badge.fury.io/py/linearmodels) | +| **Continuous Integration** | [](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) | +| | [](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) | +| **Coverage** | [](https://codecov.io/gh/bashtage/linearmodels) | +| **Code Quality** | [](https://www.codacy.com/manual/bashtage/linearmodels?utm_source=github.com&utm_medium=referral&utm_content=bashtage/linearmodels&utm_campaign=Badge_Grade) | +| | [](https://codebeat.co/projects/github-com-bashtage-linearmodels-main) | +| | [](https://lgtm.com/projects/g/bashtage/linearmodels/context:python) | +| | [](https://lgtm.com/projects/g/bashtage/linearmodels/alerts) | +| **Citation** | [](https://zenodo.org/badge/latestdoi/82291672) | + +Linear (regression) models for Python. Extends +[statsmodels](http://www.statsmodels.org) with Panel regression, +instrumental variable estimators, system estimators and models for +estimating asset prices: + +- **Panel models**: + - Fixed effects (maximum two-way) + - First difference regression + - Between estimator for panel data + - Pooled regression for panel data + - Fama-MacBeth estimation of panel models + +- **High-dimensional Regresssion**: + - Absorbing Least Squares + +- **Instrumental Variable estimators** + - Two-stage Least Squares + - Limited Information Maximum Likelihood + - k-class Estimators + - Generalized Method of Moments, also with continuously updating + +- **Factor Asset Pricing Models**: + - 2- and 3-step estimation + - Time-series estimation + - GMM estimation + +- **System Regression**: + - Seemingly Unrelated Regression (SUR/SURE) + - Three-Stage Least Squares (3SLS) + - Generalized Method of Moments (GMM) System Estimation + +Designed to work equally well with NumPy, Pandas or xarray data. + +## Panel models + +Like [statsmodels](http://www.statsmodels.org) to include, supports +formulas for specifying models. For example, the classic Grunfeld regression can be +specified + +```python +import numpy as np +from statsmodels.datasets import grunfeld +data = grunfeld.load_pandas().data +data.year = data.year.astype(np.int64) +# MultiIndex, entity - time +data = data.set_index(['firm','year']) +from linearmodels import PanelOLS +mod = PanelOLS(data.invest, data[['value','capital']], entity_effects=True) +res = mod.fit(cov_type='clustered', cluster_entity=True) +``` + +Models can also be specified using the formula interface. + +```python +from linearmodels import PanelOLS +mod = PanelOLS.from_formula('invest ~ value + capital + EntityEffects', data) +res = mod.fit(cov_type='clustered', cluster_entity=True) +``` + +The formula interface for `PanelOLS` supports the special values +`EntityEffects` and `TimeEffects` which add entity (fixed) and time +effects, respectively. + +Formula support comes from the [formulaic](https://github.com/matthewwardrop/formulaic/) +package which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/). + +## Instrumental Variable Models + +IV regression models can be similarly specified. + +```python +import numpy as np +from linearmodels.iv import IV2SLS +from linearmodels.datasets import mroz +data = mroz.load() +mod = IV2SLS.from_formula('np.log(wage) ~ 1 + exper + exper ** 2 + [educ ~ motheduc + fatheduc]', data) +``` + +The expressions in the `[ ]` indicate endogenous regressors (before `~`) +and the instruments. + +## Installing + +The latest release can be installed using pip + +```bash +pip install linearmodels +``` + +The main branch can be installed by cloning the repo and running setup + +```bash +git clone https://github.com/bashtage/linearmodels +cd linearmodels +pip install . +``` + +## Documentation + +[Stable Documentation](https://bashtage.github.io/linearmodels/) is +built on every tagged version using +[doctr](https://github.com/drdoctr/doctr). +[Development Documentation](https://bashtage.github.io/linearmodels/devel) +is automatically built on every successful build of main. + +## Plan and status + +Should eventually add some useful linear model estimators such as panel +regression. Currently only the single variable IV estimators are polished. + +- Linear Instrumental variable estimation - **complete** +- Linear Panel model estimation - **complete** +- Fama-MacBeth regression - **complete** +- Linear Factor Asset Pricing - **complete** +- System regression - **complete** +- Linear IV Panel model estimation - _not started_ +- Dynamic Panel model estimation - _not started_ + +## Requirements + +### Running + +With the exception of Python 3 (3.8+ tested), which is a hard requirement, the +others are the version that are being used in the test environment. It +is possible that older versions work. + +- Python 3.8+ +- NumPy (1.18+) +- SciPy (1.3+) +- pandas (1.0+) +- statsmodels (0.12+) +- xarray (0.16+, optional) +- Cython (0.29.24+, optional) + +### Testing + +- py.test + +### Documentation + +- sphinx +- sphinx-material +- nbsphinx +- nbconvert +- nbformat +- ipython +- jupyter + + +%prep +%autosetup -n linearmodels-4.27 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-linearmodels -f filelist.lst +%dir %{python3_sitearch}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 4.27-1 +- Package Spec generated @@ -0,0 +1 @@ +a54a92cce98caf8378c7dbb725cfb89d linearmodels-4.27.tar.gz |