Pygam

New Features
- `gridsearch(...)` allows searching across a predefined grid of points, without doing the cartesian product, when grid is a `np.ndarray` of shape `(n_points, len(flatten(gam.lam)))`. This is useful for RandomSearchCV - style behavior.

Bug Fixes
- `estimate_r_squared(X, y)` no longer raises `AttributeError`
- `dtype=auto` no longer allowed for terms
- `intercept.lam = None`

New Features
- easier global arguments for terms

python
GAM(s(0) + s(1), n_splines=10).fit(X, y)


will broadcast `n_splines=10` to all terms

Bug Fixes
- fixed inconsistencies in GAM instatiation, where


python
GAM(lam=0.6).gridsearch(X, y)


worked for multi-dimensional X

but not

python
GAM(lam=0.6).gridsearch(X, y)

New Featrues
- all GAM classes have a verbose argument. this makes them compatible with sklearn GridsearchCV + RandomizedsearchCV
- add `toy_classification` dataset
- move `generate_X_grid` to `GAM` method

Bug Fixes
- users should get a more pythonic experience with `partial_dependence` by never needing to index with i+1
- `_initial_estimate()` method no longer fails on value nudge for purely integer observations
- regenerate images
- bugs in readme
- fixes bug where poorly conditioned matrix would fail when using skcholmod
- make2d should not be verbose in `initial_estimate()`

Bug Fixes
- PoissonGAM no longer produces `-inf` log-likelihoods when using non-integer exposure.
- PoissonGAM checks exposure, weight, and y array shapes before fitting.

Bug Fixes
- **datasets are loadable** like:

from pygam.datasets load cake
X, y = cake(return_X_y=True)

- **better model initializations** for complex models by using the solution to linear unpenalized problem. This makes the second order PIRLS optimizer less likely to diverge by overshooting the maximum likelihood estimate.
- **ReadMe** call for collaboration, examples reference dataset loaders, fix typos

Bug Fixes
- bug fix in p-value for models with unknown variance. f-statistic was sensitive to estimated variance when it should be invariant.
- typos