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        "author": "Kevin Sheppard",
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            "License :: OSI Approved",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3.9",
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        "description": "# Linear Models\n\n| Metric                     |                                                                                                                                                                                                                                                          |\n| :------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| **Latest Release**         | [![PyPI version](https://badge.fury.io/py/linearmodels.svg)](https://badge.fury.io/py/linearmodels)                                                                                                                                                      |\n| **Continuous Integration** | [![Build Status](https://dev.azure.com/kevinksheppard/kevinksheppard/_apis/build/status/bashtage.linearmodels?branchName=main)](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main)                        |\n|                            | [![Build status](https://ci.appveyor.com/api/projects/status/7768doy6wrdunmdt/branch/main?svg=true)](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main)                                                                                  |\n| **Coverage**               | [![codecov](https://codecov.io/gh/bashtage/linearmodels/branch/main/graph/badge.svg)](https://codecov.io/gh/bashtage/linearmodels)                                                                                                                       |\n| **Code Quality**           | [![Codacy Badge](https://api.codacy.com/project/badge/Grade/745a24a69cb2466b95df6a53c83892de)](https://www.codacy.com/manual/bashtage/linearmodels?utm_source=github.com&utm_medium=referral&utm_content=bashtage/linearmodels&utm_campaign=Badge_Grade) |\n|                            | [![codebeat badge](https://codebeat.co/badges/aaae2fb4-72b5-4a66-97cd-77b93488f243)](https://codebeat.co/projects/github-com-bashtage-linearmodels-main)                                                                                                 |\n|                            | [![Code Quality: Python](https://img.shields.io/lgtm/grade/python/g/bashtage/linearmodels.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/bashtage/linearmodels/context:python)                                                                 |\n|                            | [![Total Alerts](https://img.shields.io/lgtm/alerts/g/bashtage/linearmodels.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/bashtage/linearmodels/alerts)                                                                                       |\n| **Citation**               | [![DOI](https://zenodo.org/badge/82291672.svg)](https://zenodo.org/badge/latestdoi/82291672)                                                                                                                                                             |\n\nLinear (regression) models for Python. Extends\n[statsmodels](http://www.statsmodels.org) with Panel regression,\ninstrumental variable estimators, system estimators and models for\nestimating asset prices:\n\n-   **Panel models**:\n    -   Fixed effects (maximum two-way)\n    -   First difference regression\n    -   Between estimator for panel data\n    -   Pooled regression for panel data\n    -   Fama-MacBeth estimation of panel models\n\n-   **High-dimensional Regresssion**:\n    -   Absorbing Least Squares\n\n-   **Instrumental Variable estimators**\n    -   Two-stage Least Squares\n    -   Limited Information Maximum Likelihood\n    -   k-class Estimators\n    -   Generalized Method of Moments, also with continuously updating\n\n-   **Factor Asset Pricing Models**:\n    -   2- and 3-step estimation\n    -   Time-series estimation\n    -   GMM estimation\n\n-   **System Regression**:\n    -   Seemingly Unrelated Regression (SUR/SURE)\n    -   Three-Stage Least Squares (3SLS)\n    -   Generalized Method of Moments (GMM) System Estimation\n\nDesigned to work equally well with NumPy, Pandas or xarray data.\n\n## Panel models\n\nLike [statsmodels](http://www.statsmodels.org) to include, supports\nformulas for specifying models. For example, the classic Grunfeld regression can be\nspecified\n\n```python\nimport numpy as np\nfrom statsmodels.datasets import grunfeld\ndata = grunfeld.load_pandas().data\ndata.year = data.year.astype(np.int64)\n# MultiIndex, entity - time\ndata = data.set_index(['firm','year'])\nfrom linearmodels import PanelOLS\nmod = PanelOLS(data.invest, data[['value','capital']], entity_effects=True)\nres = mod.fit(cov_type='clustered', cluster_entity=True)\n```\n\nModels can also be specified using the formula interface.\n\n```python\nfrom linearmodels import PanelOLS\nmod = PanelOLS.from_formula('invest ~ value + capital + EntityEffects', data)\nres = mod.fit(cov_type='clustered', cluster_entity=True)\n```\n\nThe formula interface for `PanelOLS` supports the special values\n`EntityEffects` and `TimeEffects` which add entity (fixed) and time\neffects, respectively.\n\nFormula support comes from the [formulaic](https://github.com/matthewwardrop/formulaic/)\npackage which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/).\n\n## Instrumental Variable Models\n\nIV regression models can be similarly specified.\n\n```python\nimport numpy as np\nfrom linearmodels.iv import IV2SLS\nfrom linearmodels.datasets import mroz\ndata = mroz.load()\nmod = IV2SLS.from_formula('np.log(wage) ~ 1 + exper + exper ** 2 + [educ ~ motheduc + fatheduc]', data)\n```\n\nThe expressions in the `[ ]` indicate endogenous regressors (before `~`)\nand the instruments.\n\n## Installing\n\nThe latest release can be installed using pip\n\n```bash\npip install linearmodels\n```\n\nThe main branch can be installed by cloning the repo and running setup\n\n```bash\ngit clone https://github.com/bashtage/linearmodels\ncd linearmodels\npip install .\n```\n\n## Documentation\n\n[Stable Documentation](https://bashtage.github.io/linearmodels/) is\nbuilt on every tagged version using\n[doctr](https://github.com/drdoctr/doctr).\n[Development Documentation](https://bashtage.github.io/linearmodels/devel)\nis automatically built on every successful build of main.\n\n## Plan and status\n\nShould eventually add some useful linear model estimators such as panel\nregression. Currently only the single variable IV estimators are polished.\n\n-   Linear Instrumental variable estimation - **complete**\n-   Linear Panel model estimation - **complete**\n-   Fama-MacBeth regression - **complete**\n-   Linear Factor Asset Pricing - **complete**\n-   System regression - **complete**\n-   Linear IV Panel model estimation - _not started_\n-   Dynamic Panel model estimation - _not started_\n\n## Requirements\n\n### Running\n\nWith the exception of Python 3 (3.8+ tested), which is a hard requirement, the\nothers are the version that are being used in the test environment. It\nis possible that older versions work.\n\n-   Python 3.8+\n-   NumPy (1.18+)\n-   SciPy (1.3+)\n-   pandas (1.0+)\n-   statsmodels (0.12+)\n-   xarray (0.16+, optional)\n-   Cython (0.29.24+, optional)\n\n### Testing\n\n-   py.test\n\n### Documentation\n\n-   sphinx\n-   sphinx-material\n-   nbsphinx\n-   nbconvert\n-   nbformat\n-   ipython\n-   jupyter\n\n\n",
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