text: Title: Different KDE implementations
  Body: We have experienced some issues in the past with statsmodels' KDE implementation (see in-line comments in `ridgeplot._kde.estimate_density_trace()`.
  
  - Most obvious option: [scipy.stats.gaussian_kde](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html#scipy.stats.gaussian_kde)
  - Some dedicated libraries:
    - https://github.com/LBL-EESA/fastkde
    - https://github.com/tommyod/KDEpy
  - Python 3.13 will ship a `kde()` utility as part of the built-in `statistics` module:
      - https://docs.python.org/3.13/library/statistics.html#statistics.kde
      - https://docs.python.org/3.13/library/statistics.html#sampling-from-kernel-density-estimation
      - https://github.com/python/cpython/pull/115863
      - https://github.com/python/cpython/issues/115532
  
  Things to keep in mind:
  - Backwards compatibility with the existing `ridgeplot()` arguments that are passed to statsmodels' `KDEUnivariate`
  - Performance. e.g., statsmodels provides a faster FFT implementation when using the gaussian kernel.
  - ...more?