[ENH] Add naive baseline with kernel, parametric and bounding estimators

[arnavk23]

Reference Issues/PRs

Fixes #10.

What does this implement/fix? Explain your changes.

This PR completes the remaining tasks from issue - the scikit-base rearchitecture for skpro v2:

1. Naive baseline density/distribution estimator with kernel support

• Enhanced DummyProbaRegressor with new 'kernel' strategy
• Uses Gaussian KDE (via scipy.stats.gaussian_kde) for distribution estimation

2. Composite parametric distribution estimator

• Fits parametric distribution families (Normal, Laplace, Cauchy, t, etc.)
• Automatically estimates scale parameter from training residuals
• Supports multiple distribution families with flexible parameterization

3. Bounding wrapper for predictions

• Supports lower/upper bounds with multiple bounding methods
• Methods: 'truncate' (recommended), 'clip_mean', 'delta'
• Applies bounds to predict, predict_proba, predict_interval, and predict_quantiles

Does your contribution introduce a new dependency? If yes, which one?

No.

What should a reviewer concentrate their feedback on?

• Correctness of kernel density estimation implementation and bandwidth handling
• Parametric distribution fitting logic and residual-based scale estimation
• Bounding methods implementation and edge cases (truncation, clipping, delta method)

Did you add any tests for the change?

Yes. All new estimators include:

• Unit tests for core functionality
• Test parameters for automated estimator checks

Any other comments?

PR checklist

For all contributions

• The PR title starts with either [ENH], [MNT], [DOC], or [BUG]. [BUG] - bugfix, [MNT] - CI, test framework, [ENH] - adding or improving code, [DOC] - writing or improving documentation or docstrings.

For new estimators

• I've added the estimator to the API reference - in docs/source/api_reference/taskname.rst, follow the pattern.
• I've added one or more illustrative usage examples to the docstring, in a pydocstyle compliant Examples section.
• If the estimator relies on a soft dependency, I've set the python_dependencies tag and ensured
  dependency isolation, see the estimator dependencies guide.