%global _empty_manifest_terminate_build 0
Name:		python-semopy
Version:	2.3.9
Release:	1
Summary:	Structural Equation Modeling Optimization in Python.
License:	MIT License
URL:		https://semopy.com
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/35/5b/07d80992e9bd2832cc28212d634a33786aab5fc86bf939a5806b9ca83f9a/semopy-2.3.9.tar.gz
BuildArch:	noarch


%description
# semopy
[Visit our website for a detailed introduction](https://semopy.com)


**semopy** is a Python package that includes numerous Structural Equation Modelling (SEM) techniques. 

## Features
  - Write down a model description in a user-friendly syntax
  - Estimate model's parameters using a variety of objective functions
  - Estimate models with population structure via random effects
  - Restricted Maximum Likelihood
  - Integration with gaussian processes/mixture models to tackle huge variety of phenomena
  - Calculate numerous statistics and fit indices
  - Estimate model's parameters in presence of ordinal variables
  - A vast number of settings to fit a researcher's needs
  - Fast and accurate
  - Automatically create fancy HTML reports

## Installation
**semopy** is available at PyPi and can be installed by typing the following line into terminal:

`pip install semopy`


## Syntax
To specify SEM models, The **semopy** uses the syntax, which is natural to describe regression models in R. The syntax supports three operator symbols characterising relationships between variables:

- ~ to specify structural part,
- =~ to specify measurement part,
- ~~ to specify common variance between variables.

For example, let a linear equation in the structural part of SEM model take the form:

`y = β1 x1 + β2 x2 + ε` 

Then, in **semopy** syntax it becomes:

`y ~ x1 + x2`

Parameters β1, β2 are to be estimated by **semopy**. In some cases a user might want to fix some of parameters to particular value. For instance, let's assume that we want β1 to stay equal to 2.0 and we are only interested in estimating β2:

`y ~ 2*x1 + x2`


Likewise, if a latent variable η is explained by manifest variables y1, y2, y3, then in **semopy** syntax it can be written down this way:

`eta =~ y1 + y2 + y3`

## Quickstart

The pipeline for working with SEM models in **semopy** consists of three steps:
1. Specifying a model
2. Loading a dataset.
3. Estimating parameters of the model.

Main object required for scpecifying and estimating an SEM model is `Model`.

`Model` is responsible for setting up a model from the proposed SEM syntax:
~~~
# The first step
from semopy import Model
mod = """ x1 ~ x2 + x3
          x3 ~ x2 + eta1
          eta1 =~ y1 + y2 + y3
          eta1 ~ x1
      """
model = Model(mod)
~~~
Then a dataset should be provided:
~~~
# The second step
from pandas import read_csv
data = read_csv("my_data_file.csv", index_col=0)
~~~


To estimate parameters of the model we run a `fit` method with the dataset as an argument:
~~~
# The third step
model.fit(data)
~~~

The default objective function for estimating parameters is the likelihood function and the optimisation method is SLSQP (Sequential Least-Squares Quadratic Programming). However, the *semopy* supports a wide range of other objective functions and optimisation schemes being specified as parameters in the `fit` method.

Finally, user can `inspect` parameters' estimates:

~~~
model.inspect()
~~~
## Would you like to know more?
Tutorial and overview of **semopy** features are available at the [project's website](https://semopy.com).

## Requirements
**numpy**, **pandas**, **scipy**, **sympy**, **sklearn**, **statmodels**
## Authors

* **Mescheryakov A. Georgy** - *Developer* - [georgy.m](https://gitlab.org/georgy.m) - student, SPbPU
* **Igolkina A. Anna** - *Supervisor* - [iganna](https://github.com/iganna) - Engineer, SPbPU

## License
This project is licensed under the MIT License - see the LICENSE.md file for details.



%package -n python3-semopy
Summary:	Structural Equation Modeling Optimization in Python.
Provides:	python-semopy
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-semopy
# semopy
[Visit our website for a detailed introduction](https://semopy.com)


**semopy** is a Python package that includes numerous Structural Equation Modelling (SEM) techniques. 

## Features
  - Write down a model description in a user-friendly syntax
  - Estimate model's parameters using a variety of objective functions
  - Estimate models with population structure via random effects
  - Restricted Maximum Likelihood
  - Integration with gaussian processes/mixture models to tackle huge variety of phenomena
  - Calculate numerous statistics and fit indices
  - Estimate model's parameters in presence of ordinal variables
  - A vast number of settings to fit a researcher's needs
  - Fast and accurate
  - Automatically create fancy HTML reports

## Installation
**semopy** is available at PyPi and can be installed by typing the following line into terminal:

`pip install semopy`


## Syntax
To specify SEM models, The **semopy** uses the syntax, which is natural to describe regression models in R. The syntax supports three operator symbols characterising relationships between variables:

- ~ to specify structural part,
- =~ to specify measurement part,
- ~~ to specify common variance between variables.

For example, let a linear equation in the structural part of SEM model take the form:

`y = β1 x1 + β2 x2 + ε` 

Then, in **semopy** syntax it becomes:

`y ~ x1 + x2`

Parameters β1, β2 are to be estimated by **semopy**. In some cases a user might want to fix some of parameters to particular value. For instance, let's assume that we want β1 to stay equal to 2.0 and we are only interested in estimating β2:

`y ~ 2*x1 + x2`


Likewise, if a latent variable η is explained by manifest variables y1, y2, y3, then in **semopy** syntax it can be written down this way:

`eta =~ y1 + y2 + y3`

## Quickstart

The pipeline for working with SEM models in **semopy** consists of three steps:
1. Specifying a model
2. Loading a dataset.
3. Estimating parameters of the model.

Main object required for scpecifying and estimating an SEM model is `Model`.

`Model` is responsible for setting up a model from the proposed SEM syntax:
~~~
# The first step
from semopy import Model
mod = """ x1 ~ x2 + x3
          x3 ~ x2 + eta1
          eta1 =~ y1 + y2 + y3
          eta1 ~ x1
      """
model = Model(mod)
~~~
Then a dataset should be provided:
~~~
# The second step
from pandas import read_csv
data = read_csv("my_data_file.csv", index_col=0)
~~~


To estimate parameters of the model we run a `fit` method with the dataset as an argument:
~~~
# The third step
model.fit(data)
~~~

The default objective function for estimating parameters is the likelihood function and the optimisation method is SLSQP (Sequential Least-Squares Quadratic Programming). However, the *semopy* supports a wide range of other objective functions and optimisation schemes being specified as parameters in the `fit` method.

Finally, user can `inspect` parameters' estimates:

~~~
model.inspect()
~~~
## Would you like to know more?
Tutorial and overview of **semopy** features are available at the [project's website](https://semopy.com).

## Requirements
**numpy**, **pandas**, **scipy**, **sympy**, **sklearn**, **statmodels**
## Authors

* **Mescheryakov A. Georgy** - *Developer* - [georgy.m](https://gitlab.org/georgy.m) - student, SPbPU
* **Igolkina A. Anna** - *Supervisor* - [iganna](https://github.com/iganna) - Engineer, SPbPU

## License
This project is licensed under the MIT License - see the LICENSE.md file for details.



%package help
Summary:	Development documents and examples for semopy
Provides:	python3-semopy-doc
%description help
# semopy
[Visit our website for a detailed introduction](https://semopy.com)


**semopy** is a Python package that includes numerous Structural Equation Modelling (SEM) techniques. 

## Features
  - Write down a model description in a user-friendly syntax
  - Estimate model's parameters using a variety of objective functions
  - Estimate models with population structure via random effects
  - Restricted Maximum Likelihood
  - Integration with gaussian processes/mixture models to tackle huge variety of phenomena
  - Calculate numerous statistics and fit indices
  - Estimate model's parameters in presence of ordinal variables
  - A vast number of settings to fit a researcher's needs
  - Fast and accurate
  - Automatically create fancy HTML reports

## Installation
**semopy** is available at PyPi and can be installed by typing the following line into terminal:

`pip install semopy`


## Syntax
To specify SEM models, The **semopy** uses the syntax, which is natural to describe regression models in R. The syntax supports three operator symbols characterising relationships between variables:

- ~ to specify structural part,
- =~ to specify measurement part,
- ~~ to specify common variance between variables.

For example, let a linear equation in the structural part of SEM model take the form:

`y = β1 x1 + β2 x2 + ε` 

Then, in **semopy** syntax it becomes:

`y ~ x1 + x2`

Parameters β1, β2 are to be estimated by **semopy**. In some cases a user might want to fix some of parameters to particular value. For instance, let's assume that we want β1 to stay equal to 2.0 and we are only interested in estimating β2:

`y ~ 2*x1 + x2`


Likewise, if a latent variable η is explained by manifest variables y1, y2, y3, then in **semopy** syntax it can be written down this way:

`eta =~ y1 + y2 + y3`

## Quickstart

The pipeline for working with SEM models in **semopy** consists of three steps:
1. Specifying a model
2. Loading a dataset.
3. Estimating parameters of the model.

Main object required for scpecifying and estimating an SEM model is `Model`.

`Model` is responsible for setting up a model from the proposed SEM syntax:
~~~
# The first step
from semopy import Model
mod = """ x1 ~ x2 + x3
          x3 ~ x2 + eta1
          eta1 =~ y1 + y2 + y3
          eta1 ~ x1
      """
model = Model(mod)
~~~
Then a dataset should be provided:
~~~
# The second step
from pandas import read_csv
data = read_csv("my_data_file.csv", index_col=0)
~~~


To estimate parameters of the model we run a `fit` method with the dataset as an argument:
~~~
# The third step
model.fit(data)
~~~

The default objective function for estimating parameters is the likelihood function and the optimisation method is SLSQP (Sequential Least-Squares Quadratic Programming). However, the *semopy* supports a wide range of other objective functions and optimisation schemes being specified as parameters in the `fit` method.

Finally, user can `inspect` parameters' estimates:

~~~
model.inspect()
~~~
## Would you like to know more?
Tutorial and overview of **semopy** features are available at the [project's website](https://semopy.com).

## Requirements
**numpy**, **pandas**, **scipy**, **sympy**, **sklearn**, **statmodels**
## Authors

* **Mescheryakov A. Georgy** - *Developer* - [georgy.m](https://gitlab.org/georgy.m) - student, SPbPU
* **Igolkina A. Anna** - *Supervisor* - [iganna](https://github.com/iganna) - Engineer, SPbPU

## License
This project is licensed under the MIT License - see the LICENSE.md file for details.



%prep
%autosetup -n semopy-2.3.9

%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-semopy -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 2.3.9-1
- Package Spec generated