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Kevin Sheppard
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DOC: Correct links to notebooks
closes statsmodels#8885
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‎docs/source/anova.rst

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A more detailed example for `anova_lm` can be found here:
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* `ANOVA <examples/notebooks/generated/interactions_anova.html>`__
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* `ANOVA <examples/notebooks/generated/interactions_anova.ipynb>`_
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Module Reference
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----------------

‎docs/source/discretemod.rst

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Detailed examples can be found here:
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* `Overview <examples/notebooks/generated/discrete_choice_overview.html>`__
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* `Examples <examples/notebooks/generated/discrete_choice_example.html>`__
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* `Overview <examples/notebooks/generated/discrete_choice_overview.ipynb>`_
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* `Examples <examples/notebooks/generated/discrete_choice_example.ipynb>`_
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Technical Documentation
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-----------------------

‎docs/source/glm.rst

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Detailed examples can be found here:
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* `GLM <examples/notebooks/generated/glm.html>`__
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* `Formula <examples/notebooks/generated/glm_formula.html>`__
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* `GLM <examples/notebooks/generated/glm.ipynb>`_
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* `Formula <examples/notebooks/generated/glm_formula.ipynb>`_
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Technical Documentation
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-----------------------

‎docs/source/large_data.rst

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design matrix (known as `exog`) in statsmodels, is a little challenging.
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A detailed example is available
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`here <examples/notebooks/generated/distributed_estimation.html>`_.
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`here <examples/notebooks/generated/distributed_estimation.ipynb>`_.
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.. autosummary::
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:toctree: generated/

‎docs/source/mixed_linear.rst

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Detailed examples can be found here
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* `Mixed LM <examples/notebooks/generated/mixed_lm_example.html>`__
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* `Mixed LM <examples/notebooks/generated/mixed_lm_example.ipynb>`_
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There are some notebook examples on the Wiki:
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`Wiki notebooks for MixedLM <https://github.com/statsmodels/statsmodels/wiki/Examples#linear-mixed-models>`_

‎docs/source/regression.rst

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Detailed examples can be found here:
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* `OLS <examples/notebooks/generated/ols.html>`__
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* `WLS <examples/notebooks/generated/wls.html>`__
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* `GLS <examples/notebooks/generated/gls.html>`__
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* `Recursive LS <examples/notebooks/generated/recursive_ls.html>`__
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* `Rolling LS <examples/notebooks/generated/rolling_ls.html>`__
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* `OLS <examples/notebooks/generated/ols.ipynb>`_
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* `WLS <examples/notebooks/generated/wls.ipynb>`_
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* `GLS <examples/notebooks/generated/gls.ipynb>`_
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* `Recursive LS <examples/notebooks/generated/recursive_ls.ipynb>`_
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* `Rolling LS <examples/notebooks/generated/rolling_ls.ipynb>`_
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Technical Documentation
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-----------------------

‎docs/source/rlm.rst

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Detailed examples can be found here:
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* `Robust Models 1 <examples/notebooks/generated/robust_models_0.html>`__
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* `Robust Models 2 <examples/notebooks/generated/robust_models_1.html>`__
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* `Robust Models 1 <examples/notebooks/generated/robust_models_0.ipynb>`_
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* `Robust Models 2 <examples/notebooks/generated/robust_models_1.ipynb>`_
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Technical Documentation
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-----------------------

‎docs/source/statespace.rst

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sarimax.SARIMAXResults
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For an example of the use of this model, see the
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`SARIMAX example notebook <examples/notebooks/generated/statespace_sarimax_stata.html>`__
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`SARIMAX example notebook <examples/notebooks/generated/statespace_sarimax_stata.ipynb>`_
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or the very brief code snippet below:
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structural.UnobservedComponents
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structural.UnobservedComponentsResults
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For examples of the use of this model, see the `example notebook <examples/notebooks/generated/statespace_structural_harvey_jaeger.html>`__ or a notebook on using the unobserved components model to `decompose a time series into a trend and cycle <examples/notebooks/generated/statespace_cycles.html>`__ or the very brief code snippet below:
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For examples of the use of this model, see the `example notebook <examples/notebooks/generated/statespace_structural_harvey_jaeger.ipynb>`_ or a notebook on using the unobserved components model to `decompose a time series into a trend and cycle <examples/notebooks/generated/statespace_cycles.ipynb>`_ or the very brief code snippet below:
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.. code-block:: python
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varmax.VARMAX
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varmax.VARMAXResults
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For an example of the use of this model, see the `VARMAX example notebook <examples/notebooks/generated/statespace_varmax.html>`__ or the very brief code snippet below:
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For an example of the use of this model, see the `VARMAX example notebook <examples/notebooks/generated/statespace_varmax.ipynb>`_ or the very brief code snippet below:
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.. code-block:: python
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dynamic_factor.DynamicFactorResults
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For an example of the use of the `DynamicFactor` model, see the
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`Dynamic Factor example notebook <examples/notebooks/generated/statespace_dfm_coincident.html>`__
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`Dynamic Factor example notebook <examples/notebooks/generated/statespace_dfm_coincident.ipynb>`_
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Linear Exponential Smoothing Models
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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mlemodel.PredictionResults
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For a basic example demonstrating creating and estimating a custom state space
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model, see the `Local Linear Trend example notebook <examples/notebooks/generated/statespace_local_linear_trend.html>`__.
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model, see the `Local Linear Trend example notebook <examples/notebooks/generated/statespace_local_linear_trend.ipynb>`_.
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For a more sophisticated example, see the source code for the `SARIMAX` and
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`SARIMAXResults` classes, which are built by extending `MLEModel` and
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`MLEResults`.

‎docs/source/stats.rst

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Statistical functions for rates. This currently includes hypothesis tests for
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two independent samples.
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See also example notebook for an overview
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`Poisson Rates <examples/notebooks/generated/stats_poisson.html>`__
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`Poisson Rates <examples/notebooks/generated/stats_poisson.ipynb>`_
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Status: experimental, API might change, added in 0.12, refactored and enhanced
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Examples can be found in the notebook
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* `Meta-Analysis <examples/notebooks/generated/metaanalysis1.html>`__
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* `Meta-Analysis <examples/notebooks/generated/metaanalysis1.ipynb>`_
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Status: experimental, API might change, added in 0.12
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‎docs/source/treatment.rst

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for some methods optionally average treatment effect on the treated ATT.
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See also overview notebook in
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`Treatment Effect <examples/notebooks/generated/treatment_effect.html>`__
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`Treatment Effect <examples/notebooks/generated/treatment_effect.ipynb>`_
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.. module:: statsmodels.treatment
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.. currentmodule:: statsmodels.treatment

‎docs/source/tsa.rst

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`SARIMAX` class (using full MLE via the Kalman Filter).
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See the notebook `Autoregressions
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<../examples/notebooks/generated/autoregressions.html>`__ for an overview.
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<examples/notebooks/generated/autoregressions.ipynb>`_ for an overview.
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Autoregressive Moving-Average Processes (ARMA) and Kalman Filter
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See the notebooks `ARMA: Sunspots Data
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<../examples/notebooks/generated/tsa_arma_0.html>`__ and
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`ARMA: Artificial Data <../examples/notebooks/generated/tsa_arma_1.html>`__
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<examples/notebooks/generated/tsa_arma_0.ipynb>`_ and
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`ARMA: Artificial Data <examples/notebooks/generated/tsa_arma_1.ipynb>`_
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statespace.exponential_smoothing.ExponentialSmoothingResults
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See the notebook `Exponential Smoothing
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<../examples/notebooks/generated/exponential_smoothing.html>`__
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<examples/notebooks/generated/exponential_smoothing.ipynb>`_
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ARMA Process
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<../examples/notebooks/generated/autoregressive_distributed_lag.html>`__
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<examples/notebooks/generated/autoregressive_distributed_lag.ipynb>`_
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See the notebooks `Markov switching dynamic regression
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<../examples/notebooks/generated/markov_regression.html>`__ and
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<examples/notebooks/generated/markov_regression.ipynb>`_ and
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`Markov switching autoregression
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<../examples/notebooks/generated/markov_autoregression.html>`__
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<examples/notebooks/generated/markov_autoregression.ipynb>`_
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DecomposeResult
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<examples/notebooks/generated/tsa_filters.ipynb>`_ for an overview.
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TSA Tools
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<../examples/notebooks/generated/deterministics.html>`__ for an overview.
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<examples/notebooks/generated/deterministics.ipynb>`_ for an overview.
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Forecasting Models
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<examples/notebooks/generated/stl_decomposition.ipynb>`_ for an overview.
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Prediction Results
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""""""""""""""""""

‎requirements-dev.txt

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# build
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cython>=0.29.28,<3.0.0
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setuptools_scm[toml]~=7.0.0
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setuptools_scm[toml]~=7.0
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# test
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pytest~=7.0.1
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pytest>=7.3.0
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pytest-randomly
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pytest-xdist
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