%global _empty_manifest_terminate_build 0 Name: python-formulae Version: 0.3.4 Release: 1 Summary: Formulas for mixed-effects models in Python License: MIT URL: https://github.com/bambinos/formulae Source0: https://mirrors.nju.edu.cn/pypi/web/packages/43/a7/4a3043b24f76d318eb6f12464aca4daef39c23630803eeb6acbae8a78fc3/formulae-0.3.4.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-pandas Requires: python3-scipy %description [![PyPI version](https://badge.fury.io/py/formulae.svg)](https://badge.fury.io/py/formulae) [![codecov](https://codecov.io/gh/bambinos/formulae/branch/master/graph/badge.svg)](https://codecov.io/gh/bambinos/formulae) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) # formulae formulae is a Python library that implements Wilkinson's formulas for mixed-effects models. The main difference with other implementations like [Patsy](https://github.com/pydata/patsy) or [formulaic](https://github.com/matthewwardrop/formulaic) is that formulae can work with formulas describing a model with both common and group specific effects (a.k.a. fixed and random effects, respectively). This package has been written to make it easier to specify models with group effects in [Bambi](https://github.com/bambinos/bambi), a package that makes it easy to work with Bayesian GLMMs in Python, but it could be used independently as a backend for another library. The approach in this library is to extend classical statistical formulas in a similar way than in R package [lme4](https://CRAN.R-project.org/package=lme4). ## Installation formulae requires a working Python interpreter (3.7+) and the libraries numpy, scipy and pandas with versions specified in the [requirements.txt](https://github.com/bambinos/formulae/blob/master/requirements.txt) file. Assuming a standard Python environment is installed on your machine (including pip), the latest release of formulae can be installed in one line using pip: `pip install formulae` Alternatively, if you want the development version of the package you can install from GitHub: `pip install git+https://github.com/bambinos/formulae.git` ## Documentation The official documentation can be found [here](https://bambinos.github.io/formulae) ## Notes - The `data` argument only accepts objects of class `pandas.DataFrame`. - `y ~ .` is not implemented and won't be implemented in a first version. However, it is planned to be included in the future. %package -n python3-formulae Summary: Formulas for mixed-effects models in Python Provides: python-formulae BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-formulae [![PyPI version](https://badge.fury.io/py/formulae.svg)](https://badge.fury.io/py/formulae) [![codecov](https://codecov.io/gh/bambinos/formulae/branch/master/graph/badge.svg)](https://codecov.io/gh/bambinos/formulae) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) # formulae formulae is a Python library that implements Wilkinson's formulas for mixed-effects models. The main difference with other implementations like [Patsy](https://github.com/pydata/patsy) or [formulaic](https://github.com/matthewwardrop/formulaic) is that formulae can work with formulas describing a model with both common and group specific effects (a.k.a. fixed and random effects, respectively). This package has been written to make it easier to specify models with group effects in [Bambi](https://github.com/bambinos/bambi), a package that makes it easy to work with Bayesian GLMMs in Python, but it could be used independently as a backend for another library. The approach in this library is to extend classical statistical formulas in a similar way than in R package [lme4](https://CRAN.R-project.org/package=lme4). ## Installation formulae requires a working Python interpreter (3.7+) and the libraries numpy, scipy and pandas with versions specified in the [requirements.txt](https://github.com/bambinos/formulae/blob/master/requirements.txt) file. Assuming a standard Python environment is installed on your machine (including pip), the latest release of formulae can be installed in one line using pip: `pip install formulae` Alternatively, if you want the development version of the package you can install from GitHub: `pip install git+https://github.com/bambinos/formulae.git` ## Documentation The official documentation can be found [here](https://bambinos.github.io/formulae) ## Notes - The `data` argument only accepts objects of class `pandas.DataFrame`. - `y ~ .` is not implemented and won't be implemented in a first version. However, it is planned to be included in the future. %package help Summary: Development documents and examples for formulae Provides: python3-formulae-doc %description help [![PyPI version](https://badge.fury.io/py/formulae.svg)](https://badge.fury.io/py/formulae) [![codecov](https://codecov.io/gh/bambinos/formulae/branch/master/graph/badge.svg)](https://codecov.io/gh/bambinos/formulae) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) # formulae formulae is a Python library that implements Wilkinson's formulas for mixed-effects models. The main difference with other implementations like [Patsy](https://github.com/pydata/patsy) or [formulaic](https://github.com/matthewwardrop/formulaic) is that formulae can work with formulas describing a model with both common and group specific effects (a.k.a. fixed and random effects, respectively). This package has been written to make it easier to specify models with group effects in [Bambi](https://github.com/bambinos/bambi), a package that makes it easy to work with Bayesian GLMMs in Python, but it could be used independently as a backend for another library. The approach in this library is to extend classical statistical formulas in a similar way than in R package [lme4](https://CRAN.R-project.org/package=lme4). ## Installation formulae requires a working Python interpreter (3.7+) and the libraries numpy, scipy and pandas with versions specified in the [requirements.txt](https://github.com/bambinos/formulae/blob/master/requirements.txt) file. Assuming a standard Python environment is installed on your machine (including pip), the latest release of formulae can be installed in one line using pip: `pip install formulae` Alternatively, if you want the development version of the package you can install from GitHub: `pip install git+https://github.com/bambinos/formulae.git` ## Documentation The official documentation can be found [here](https://bambinos.github.io/formulae) ## Notes - The `data` argument only accepts objects of class `pandas.DataFrame`. - `y ~ .` is not implemented and won't be implemented in a first version. However, it is planned to be included in the future. %prep %autosetup -n formulae-0.3.4 %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-formulae -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 10 2023 Python_Bot - 0.3.4-1 - Package Spec generated