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        "description": "# semopy\n[![Documentation Status](https://readthedocs.org/projects/semopy/badge/?version=latest)](https://semopy.readthedocs.io/en/latest/?badge=latest)\n\n\n**semopy** is an umbrella Python package that includes numerous Structural Equation Modelling (SEM) techniques. \n\n## Features\n  - Write down a model description in a user-friendly syntax\n  - Estimate model's parameters using a variety of objective functions\n  - Calculate numerous statistics and fit indices\n  - Estimate model's parameters in presence of ordinal variables\n  - A vast number of settings to fit a researcher's needs\n  - Fast and accurate\n\n## Installation\n**semopy** is available at PyPi and can be installed by typing the following line into terminal:\n\n`pip install semopy`\n\n\n## Syntax\nTo 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:\n\n- ~ to specify structural part,\n- =~ to specify measurement part,\n- ~~ to specify common variance between variables.\n\nFor example, let a linear equation in the structural part of SEM model take the form:\n\n`y = \u03b21 x1 + \u03b22 x2 + \u03b5` \n\nThen, in **semopy** syntax it becomes:\n\n`y ~ x1 + x2`\n\nParameters \u03b21, \u03b22 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 \u03b21 to stay equal to 2.0 and we are only interested in estimating \u03b22:\n\n`y ~ 2*x1 + x2`\n\n\nLikewise, if a latent variable \u03b7 is explained by manifest variables y1, y2, y3, then in **semopy** syntax it can be written down this way:\n\n`eta =~ y1 + y2 + y3`\n\nIt is also possible to specify a type of variable. If variable x2 is ordinal, we can inform package about it by using a special operator \"is\":\n\n`x2 is ordinal`\n\n\n## Quickstart\nFor Jupyter notebooks examples with real data see [Examples](https://bitbucket.org/herrberg/semopy/src/master/examples/) directory.\n\nThe pipeline for working with SEM models in **semopy** consists of three steps:\n1. Specifying a model\n2. Loading a dataset to the model\n3. Estimating parameters of the model.\n\nTwo main objects required for scpecifying and estimating an SEM model are *Model* and *Optimizer*.\n\n*Model* is responsible for setting up a model from the proposed SEM syntax:\n~~~\n# The first step\nfrom semopy import Model\nmod = \"\"\" x1 ~ x2 + x3\n          x3 ~ x2 + eta1\n          eta1 =~ y1 + y2 + y3\n          eta1 ~ x1\n      \"\"\"\nmodel = Model(mod)\n~~~\nThen a dataset should be provided; at this step the initial values of parameters are calculated:\n~~~\n# The second step\nfrom pandas import read_csv\ndata = read_csv(\"my_data_file.csv\", index_col=0)\nmodel.load_dataset(data)\n~~~\n\n\nTo estimate parameters of the model an *Optimizer* object should be initialised and estimation executed:\n~~~\n# The third step\nfrom semopy import Optimizer\nopt = Optimizer(model)\nobjective_function_value = opt.optimize()\n~~~\n\nThe 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 *optimize* method.\n\nFinally, user can inspect parameters' estimates:\n\n~~~\nfrom semopy.inspector import inspect\ninspect(opt)\n~~~\n\n## Documentation\nAll methods and classes have a docstring provided and a **Sphinx**-generated documentation is also available at [ReadTheDocs](semopy.readthedocs.io). For more details see an article.\n\n\n## Requirements\n**numpy**, **pandas**, **scipy**, **portmin** (our **portmin** [port](pypi.org/portmin), also requires **Cython**).\n\n## Authors\n\n* **Mescheryakov A. Georgy** - *Developer* - [Herrberg](https://bitbucket.org/herrberg) - undegraduate student, SPbSTU\n* **Igolkina A. Anna** - *Supervisor* - [iganna](https://github.com/iganna) - PhD, SPbSTU\n\n## License\nThis project is licensed under the MIT License - see the LICENSE.md file for details.",
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