Release v0.2.0

Version 0.2.0 (2021-04-02)

New Features

• Added the morphological and mollified (mormol) function to pybaselines.morphological,
  which uses a combination of morphology for baseline estimation and mollification for
  smoothing.
• Added the loess function to pybaselines.polynomial, which does local robust polynomial
  fitting. Allows using symmetric or asymmetric weighting, or using thresholding, similar
  to the modpoly and imodpoly functions.
• Added the penalized_poly function to pybaselines.polynomial, which fits a polynomial baseline
  using a non=quadratic cost function. The non=quadratic cost functions include
  huber, truncated=quadratic, and indec, and can be either symmetric or asymmetric.
• Added options for padding data when doing convolution or window=based
  operations to reduce edge effects and give better results.

Bug Fixes

• Fixed the mollification kernel used for the morphological.iamor (now amormol) function.
• Fixed a miscalculation with the weighting for whittaker.aspls.

Other Changes

• Slightly sped up several functions in whittaker.py by precomputing terms.
• Added tests for all baseline algorithms

Deprecations/Breaking Changes

• Renamed morphology.iamor to morphology.amormol (averaging morphological and
  mollified baseline) to make it more clear that mormol and amormol are similar methods.
• Renamed penalized_least_squares.py to whittaker.py, to be more specific, since other
  techniques also use penalized least squares for polynomial fitting.

Documentation/Examples

• Updated the example program to match the changes to pybaselines.
• Setup initial documentation.