Function - Kernel Density Estimation (KDE)

2021年4月4日

"In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable."
from wikipedia.com

KDE function with optional kernel:

• Uniform
  
• Triangle
  
• Epanechnikov
  
• Quartic
  
• Triweight
  
• Gaussian
  
• Cosinus
  

Republishing due to change of function.
deprecated script:

2021年4月4日

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added quartic and triweight kernels.

2021年4月5日

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• added placeholder for kernels(logistic, sigmoid, silverman)
  
• added kernel calculations for kernel(uniform, triangular, cosine)
  

2021年4月5日

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added calculations for kernels(logistic, sigmoid and silverman(Not working atm)

2021年4月7日

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removed silverman kernel, added highest value index line/label, nearest to 0 index as a dotted gray line.

2021年4月21日

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added extra stats/visuals to drawing function.