Linearmodels

This release fixes a small number of bugs, adds new measure of model fit, and provides some essential maintenance:

* Blackened the code.
* Added McElroy's and Berndt's measures of system fit.
* Removed support for Python 3.5 inline with NEP-29.
* Fixed a packing error in 4.15.2

This release fixes a small number of bugs, adds new measure of model fit, and provides some essential maintenance:

* Blackened the code.
* Added McElroy's and Berndt's measures of system fit.
* Removed support for Python 3.5 inline with NEP-29.
* Fixed a packing error in 4.15.1

This release fixes a small number of bugs, adds new measure of model fit, and provides some essential maintenance:

* Blackened the code.
* Added McElroy's and Berndt's measures of system fit.
* Removed support for Python 3.5 inline with NEP-29.
* Fixed a packing error in 4.15

This release fixes a small number of bugs, adds new measure of model fit, and provides some essential maintenance:

* Blackened the code.
* Added McElroy's and Berndt's measures of system fit.
* Removed support for Python 3.5 inline with NEP-29.

This release fixes a small number of bugs and provides some essential maintenance:

* Fixed issue where datasets were not installed with wheels.
* Switched to property-cached to inherit cached property from property.
* Removed all use pandas.Panel.

This is a feature and bug release.

* Added `AbsorbingLS` which allows a large number of variables to be absorbed. This model can handle very high-dimensional dummy variables and has been tested using up to 1,000,000 categories in a data set
with 5,000,000 observations.
* Fixed a bug when estimating weighted panel models that have repeated observations (i.e., more than one observation per entity and time id).
* Added ``drop_absorbed`` option to `PanelOLS` which automatically drops variables that are absorbed by fixed effects.
* Added optional Cythonized node selection for dropping singletons
* Added preconditioning to the dummy variable matrix when ``use_lsmr=True`` in `fit`. In models with many effects, this can reduce run time by a factor of 4 or more.