Implement two new scipy-compatible distributions : Log-Nakagami
(lognakagami) and Log-Gamma (loggamma_dist), with complete
logpdf/cdf/ppf/stats/entropy/rvs methods derived from the
change-of-variable Y = ln(X).
Add kl_statistic, a KDE-based KL-divergence goodness-of-fit callable
compatible with the Fitter class. Extend k_gen with _stats (improving speed), _cdf, and
a fit guard, and switch kv → kve to improve numerical stability at large arguments.
Add unit tests for all three additions covering normalization,
monotonicity, ppf inversion, moment formulas, and Fitter integration.
`generate_data.py` for processing and fitting statistical distributions to data.
'distributions.py' to create new dists to fit
'analysis_data.ipynb" notebook for data analysis
