DOC: Fix doc build errors

Fix errors related to versions strings
Revert travis package install locations

.travis.yml

@@ -52,10 +52,10 @@ before_install:
   - conda create -n linearmodels-test python=${PYTHON} numpy=${NUMPY} scipy=${SCIPY} pandas=${PANDAS} statsmodels matplotlib seaborn
   - source activate linearmodels-test
   - if [[ ${XARRAY} = true ]]; then conda install xarray; fi
-  - conda install --yes --quiet sphinx ipython jupyter nbconvert nbformat pytest pytest-xdist coverage pytest-cov
-  - pip install codecov doctr nbsphinx guzzle_sphinx_theme -q
-  # - pip install --upgrade --no-deps sphinx
-  # - pip install sphinx
+  - conda install --yes --quiet sphinx ipython jupyter nbconvert nbformat
+  - pip install pytest pytest-xdist coverage pytest-cov codecov sphinx doctr nbsphinx guzzle_sphinx_theme -q
+  - pip install --upgrade --no-deps sphinx
+  - pip install sphinx
   - export PYTHONHASHSEED=0
   - export MKL_NUM_THREADS=1
 

MANIFEST.in

@@ -1,13 +1,13 @@
-global-include *.csv *.py *.dta *.ipynb
+global-include *.csv *.py *.dta *.ipynb *.png
 
 include versioneer.py
 include linearmodels/_version.py
 include README.rst
 include README.md
 include requirements.txt
-include requirements-test-doc.txt
+include requirements-dev.txt
 include examples/*.ipynb
 recursive-include doc/source *
-recursive-include linearmodels/iv/tests *
+recursive-include linearmodels/tests *
 recursive-include linearmodels/datasets *
 recursive-include examples *

README.rst

@@ -1,164 +1,164 @@
-Linear Models
-=============
-
-|Build Status| |codecov|
-
-Linear (regression) models for Python. Extends
-`statsmodels <http://www.statsmodels.org>`__ with Panel regression,
-instrumental variable estimators, system estimators and models for
-estimating asset prices:
-
--  **Panel models**:
-
-   -  Fixed effects (maximum two-way)
-   -  First difference regression
-   -  Between estimator for panel data
-   -  Pooled regression for panel data
-   -  Fama-MacBeth estimation of panel models
-
--  **Instrumental Variable estimators**
-
-   -  Two-stage Least Squares
-   -  Limited Information Maximum Likelihood
-   -  k-class Estimators
-   -  Generalized Method of Moments, also with continuously updating
-
--  **Factor Asset Pricing Models**:
-
-   -  2- and 3-step estimation
-   -  Time-series estimation
-   -  GMM estimation
-
--  **System Regression**:
-
-   -  Seemingly Unrelated Regression (SUR/SURE)
-   -  Three-Stage Least Squares (3SLS)
-
-Designed to work equally well with NumPy, Pandas or xarray data.
-
-Panel models
-~~~~~~~~~~~~
-
-Like `statsmodels <http://www.statsmodels.org>`__ to include, supports
-`patsy <https://patsy.readthedocs.io/en/latest/>`__ formulas for
-specifying models. For example, the classic Grunfeld regression can be
-specified
-
-.. code:: python
-
-    import numpy as np
-    from statsmodels.datasets import grunfeld
-    data = grunfeld.load_pandas().data
-    data.year = data.year.astype(np.int64)
-    # MultiIndex, entity - time
-    data = data.set_index(['firm','year'])
-    from linearmodels import PanelOLS
-    mod = PanelOLS(data.invest, data[['value','capital']], entity_effect=True)
-    res = mod.fit(cov_type='clustered', cluster_entity=True)
-
-Models can also be specified using the formula interface.
-
-.. code:: python
-
-    from linearmodels import PanelOLS
-    mod = PanelOLS.from_formula('invest ~ value + capital + EntityEffect', data)
-    res = mod.fit(cov_type='clustered', cluster_entity=True)
-
-The formula interface for ``PanelOLS`` supports the special values
-``EntityEffects`` and ``TimeEffects`` which add entity (fixed) and time
-effects, respectively.
-
-Instrumental Variable Models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-IV regression models can be similarly specified.
-
-.. code:: python
-
-    import numpy as np
-    from linearmodels.iv import IV2SLS
-    from linearmodels.datasets import mroz
-    data = mroz.load()
-    mod = IV2SLS.from_formula('np.log(wage) ~ 1 + exper + exper ** 2 + [educ ~ motheduc + fatheduc]', data)
-
-The expressions in the ``[ ]`` indicate endogenous regressors (before
-``~``) and the instruments.
-
-Installing
-----------
-
-The latest release can be installed using pip
-
-.. code:: bash
-
-    pip install linearmodels
-
-The master branch can be installed by cloning the repo and running setup
-
-.. code:: bash
-
-    git clone https://github.com/bashtage/linearmodels
-    cd linearmodels
-    python setup.py install
-
-Documentation
--------------
-
-`Stable Documentation <https://bashtage.github.io/linearmodels/doc>`__
-is built on every tagged version using
-`doctr <https://github.com/drdoctr/doctr>`__. `Development
-Documentation <https://bashtage.github.io/linearmodels/devel>`__ is
-automatically built on every successful build of master.
-
-Plan and status
----------------
-
-Should eventually add some useful linear model estimators such as panel
-regression. Currently only the single variable IV estimators are
-polished.
-
--  Linear Instrumental variable estimation - **complete**
--  Linear Panel model estimation - **complete**
--  Fama-MacBeth regression - **complete**
--  Linear Factor Asset Pricing - **complete**
--  System regression - **complete**
--  Linear IV Panel model estimation - *not started*
--  Dynamic Panel model estimation - *not started*
-
-Requirements
-------------
-
-Running
-~~~~~~~
-
-With the exception of Python 3.5+, which is a hard requirement, the
-others are the version that are being used in the test environment. It
-is possible that older versions work.
-
--  **Python 3.5+**: extensive use of ``@`` operator
--  NumPy (1.11+)
--  SciPy (0.18+)
--  pandas (0.19+)
--  statsmodels (0.8+)
--  xarray (0.9+, optional)
-
-Testing
-~~~~~~~
-
--  py.test
-
-Documentation
-~~~~~~~~~~~~~
-
--  sphinx
--  guzzle\_sphinx\_theme
--  nbsphinx
--  nbconvert
--  nbformat
--  ipython
--  jupyter
-
-.. |Build Status| image:: https://travis-ci.org/bashtage/linearmodels.svg?branch=master
-   :target: https://travis-ci.org/bashtage/linearmodels
-.. |codecov| image:: https://codecov.io/gh/bashtage/linearmodels/branch/master/graph/badge.svg
-   :target: https://codecov.io/gh/bashtage/linearmodels
+Linear Models
+=============
+
+|Build Status| |codecov|
+
+Linear (regression) models for Python. Extends
+`statsmodels <http://www.statsmodels.org>`__ with Panel regression,
+instrumental variable estimators, system estimators and models for
+estimating asset prices:
+
+-  **Panel models**:
+
+   -  Fixed effects (maximum two-way)
+   -  First difference regression
+   -  Between estimator for panel data
+   -  Pooled regression for panel data
+   -  Fama-MacBeth estimation of panel models
+
+-  **Instrumental Variable estimators**
+
+   -  Two-stage Least Squares
+   -  Limited Information Maximum Likelihood
+   -  k-class Estimators
+   -  Generalized Method of Moments, also with continuously updating
+
+-  **Factor Asset Pricing Models**:
+
+   -  2- and 3-step estimation
+   -  Time-series estimation
+   -  GMM estimation
+
+-  **System Regression**:
+
+   -  Seemingly Unrelated Regression (SUR/SURE)
+   -  Three-Stage Least Squares (3SLS)
+
+Designed to work equally well with NumPy, Pandas or xarray data.
+
+Panel models
+~~~~~~~~~~~~
+
+Like `statsmodels <http://www.statsmodels.org>`__ to include, supports
+`patsy <https://patsy.readthedocs.io/en/latest/>`__ formulas for
+specifying models. For example, the classic Grunfeld regression can be
+specified
+
+.. code:: python
+
+    import numpy as np
+    from statsmodels.datasets import grunfeld
+    data = grunfeld.load_pandas().data
+    data.year = data.year.astype(np.int64)
+    # MultiIndex, entity - time
+    data = data.set_index(['firm','year'])
+    from linearmodels import PanelOLS
+    mod = PanelOLS(data.invest, data[['value','capital']], entity_effect=True)
+    res = mod.fit(cov_type='clustered', cluster_entity=True)
+
+Models can also be specified using the formula interface.
+
+.. code:: python
+
+    from linearmodels import PanelOLS
+    mod = PanelOLS.from_formula('invest ~ value + capital + EntityEffect', data)
+    res = mod.fit(cov_type='clustered', cluster_entity=True)
+
+The formula interface for ``PanelOLS`` supports the special values
+``EntityEffects`` and ``TimeEffects`` which add entity (fixed) and time
+effects, respectively.
+
+Instrumental Variable Models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+IV regression models can be similarly specified.
+
+.. code:: python
+
+    import numpy as np
+    from linearmodels.iv import IV2SLS
+    from linearmodels.datasets import mroz
+    data = mroz.load()
+    mod = IV2SLS.from_formula('np.log(wage) ~ 1 + exper + exper ** 2 + [educ ~ motheduc + fatheduc]', data)
+
+The expressions in the ``[ ]`` indicate endogenous regressors (before
+``~``) and the instruments.
+
+Installing
+----------
+
+The latest release can be installed using pip
+
+.. code:: bash
+
+    pip install linearmodels
+
+The master branch can be installed by cloning the repo and running setup
+
+.. code:: bash
+
+    git clone https://github.com/bashtage/linearmodels
+    cd linearmodels
+    python setup.py install
+
+Documentation
+-------------
+
+`Stable Documentation <https://bashtage.github.io/linearmodels/doc>`__
+is built on every tagged version using
+`doctr <https://github.com/drdoctr/doctr>`__. `Development
+Documentation <https://bashtage.github.io/linearmodels/devel>`__ is
+automatically built on every successful build of master.
+
+Plan and status
+---------------
+
+Should eventually add some useful linear model estimators such as panel
+regression. Currently only the single variable IV estimators are
+polished.
+
+-  Linear Instrumental variable estimation - **complete**
+-  Linear Panel model estimation - **complete**
+-  Fama-MacBeth regression - **complete**
+-  Linear Factor Asset Pricing - **complete**
+-  System regression - **complete**
+-  Linear IV Panel model estimation - *not started*
+-  Dynamic Panel model estimation - *not started*
+
+Requirements
+------------
+
+Running
+~~~~~~~
+
+With the exception of Python 3.5+, which is a hard requirement, the
+others are the version that are being used in the test environment. It
+is possible that older versions work.
+
+-  **Python 3.5+**: extensive use of ``@`` operator
+-  NumPy (1.11+)
+-  SciPy (0.18+)
+-  pandas (0.19+)
+-  statsmodels (0.8+)
+-  xarray (0.9+, optional)
+
+Testing
+~~~~~~~
+
+-  py.test
+
+Documentation
+~~~~~~~~~~~~~
+
+-  sphinx
+-  guzzle\_sphinx\_theme
+-  nbsphinx
+-  nbconvert
+-  nbformat
+-  ipython
+-  jupyter
+
+.. |Build Status| image:: https://travis-ci.org/bashtage/linearmodels.svg?branch=master
+   :target: https://travis-ci.org/bashtage/linearmodels
+.. |codecov| image:: https://codecov.io/gh/bashtage/linearmodels/branch/master/graph/badge.svg
+   :target: https://codecov.io/gh/bashtage/linearmodels

doc/source/conf.py

@@ -82,14 +82,15 @@
 # The short X.Y version.
 # The short X.Y version.
 version = LooseVersion(linearmodels.__version__)
-if len(version.version) > 2:
-    version = linearmodels.__version__.split('.')[:3]
-    version = '.'.join(version).split('+')
-    tag = ' (+' + version[1] + ')'
-    tag = ', '.join(tag.split('.'))
-    version = version[0] + tag
+if '+' in version.version:
+    version = linearmodels.__version__.split('+')
+    commits, tag = version[1].split('.')
+    version = version[0]
+    tag = ' (+' + commits + ', ' + tag + ')'
+    version = version + tag
 else:
     version = linearmodels.__version__
+
 # The full version, including alpha/beta/rc tags.
 release = linearmodels.__version__