glum 2.0.0

Breaking changes:

• Renamed the package to glum!!! Hurray! Celebration.
• GeneralizedLinearRegressor and GeneralizedLinearRegressorCV lose the fit_dispersion parameter.
  Please use the dispersion method of the appropriate family instance instead.
• All functions now use sample_weight as a keyword instead of weights, in line with scikit-learn.
• All functions now use dispersion as a keyword instead of phi.
• Several methods GeneralizedLinearRegressor and GeneralizedLinearRegressorCV that should have been private have had an underscore prefixed on their names: tear_down_from_fit, _set_up_for_fit, _set_up_and_check_fit_args, _get_start_coef, _solve and _solve_regularization_path.
• glum.GeneralizedLinearRegressor.report_diagnostics and glum.GeneralizedLinearRegressor.get_formatted_diagnostics are now public.

New features:

• P1 and P2 now accepts 1d array with the same number of elements as the unexpanded design matrix. In this case,
  the penalty associated with a categorical feature will be expanded to as many elements as there are levels,
  all with the same value.
• ExponentialDispersionModel gains a dispersion method.
• BinomialDistribution and TweedieDistribution gain a log_likelihood method.
• The fit method of GeneralizedLinearRegressor and GeneralizedLinearRegressorCV
  now saves the column types of pandas data frames.
• GeneralizedLinearRegressor and GeneralizedLinearRegressorCV gain two properties: family_instance and link_instance.
• GeneralizedLinearRegressor.std_errors and GeneralizedLinearRegressor.covariance_matrix have been added and support non-robust, robust (HC-1), and clustered
  covariance matrices.
• GeneralizedLinearRegressor and GeneralizedLinearRegressorCV now accept family='gaussian' as an alternative to family='normal'.

Bug fix:

• The score method of GeneralizedLinearRegressor and GeneralizedLinearRegressorCV now accepts data frames.
• Upgraded the code to use tabmat 3.0.0.

Other:

• A major overhaul of the documentation. Everything is better!
• The methods of the link classes will now return scalars when given scalar inputs. Under certain circumstances, they'd return zero-dimensional arrays.
• There is a new benchmark available glm_benchmarks_run based on the Boston housing dataset. See here.
• glm_benchmarks_analyze now includes offset in the index. See here.
• glmnet_python was removed from the benchmarks suite.
• The innermost coordinate descent was optimized. This speeds up coordinate descent dominated problems like LASSO by about 1.5-2x. See here.