Doubt

Added
- Added a `verbose` argument to `QuantileRegressionForest`, which displays a
progress bar during training.

Changed
- The default value of `QuantileRegressionForest.min_samples_leaf` has changed
from 1 to 5, to ensure that the quantiles can always be computed sensibly
with the default setting.

Fixed
- The `logkow` feature in the `FishBioconcentration` dataset is now converted
into a float, rather than a string.
- Typo in example script in `README`

Removed
- `QuantileLinearRegression` has been removed, and `QuantileRegressor` should
be used instead

Added
- Added `quantiles` argument to `QuantileRegressionTree` and `Boot`, as an
alternative to specifying `uncertainty`, if you want to return specific
quantiles.
- Added general `QuantileRegressor`, which can wrap any general linear model
for quantile predictions.

Fixed
- The predictions in `Boot.predict` were based on a fitting of the model to one
of the bootstrapped datasets. It is now based on the entire dataset, which in
particular means that the predictions will be deterministic. The intervals
will still be stochastic, as they should be.

Changed
- Updated Numpy random number generation to [their new API](https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator)
- All residuals in `Boot` are now calculated during fitting, which should
decrease the prediction times a tiny bit.

Removed
- Package no longer relies on `statsmodels`

Fixed
- A handful of docstring style changes to yield a cleaner Sphinx documentation

Added
- Sphinx documentation

Added
- Implemented `score` method to `QuantileLinearRegression`, which either
outputs the mean negative pinball loss function, or the R^2 value
- Added more documentation to `QuantileLinearRegression`

Changed
- Outputs more informative error message when a singular feature matrix is
being used with `QuantileLinearRegression`
- Datasets look prettier in notebooks now

Removed
- Removed docstring comments about closing datasets after use, as this is
automatic