Links:

• https://en.wikipedia.org/wiki/Smoothing
• https://terpconnect.umd.edu/~toh/spectrum/Smoothing.html
• http://de.mathworks.com/help/curvefit/smoothing-data.html
• http://de.mathworks.com/help/curvefit/smooth.html
• http://www.originlab.com/doc/Origin-Help/Smoothing
• http://mathematica.stackexchange.com/questions/61786/how-can-i-obtain-a-smooth-curve-from-my-data-but-preserve-two-inversion-points
• http://cubic.org/docs/bezier.htm
• LOWESS:
  • http://www.itl.nist.gov/div898/handbook/pmd/section1/pmd144.htm
  • http://de.mathworks.com/help/curvefit/smoothing-data.html#bq_6ys3-3
  • http://statsmodels.sourceforge.net/devel/generated/statsmodels.nonparametric.smoothers_lowess.lowess.html
  • https://gist.github.com/agramfort/850437
  • http://slendermeans.org/lowess-speed.html
  • http://svn.r-project.org/R/trunk/src/library/stats/src/lowess.c
  • https://scion.duhs.duke.edu/svn/vespa/tags/0_2_0/libduke_mr/lowess.py
  • https://github.com/hroest/CppLowess

Smoothing methods:

• Running/Moving average with weight (centered/lagged) - DONE
• Percentile filter - DONE
• Savitzky-Golay - DONE
  • TODO: differentiation

TODO:

• LOESS/LOWESS/RLOESS/RLOWESS
• Bezier curves (de Casteljau)
• B-spline (de Boor, see GSL)
• ( Fourier low pass filter)