Function - Kernel Density Estimation (KDE)

"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:

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added quartic and triweight kernels.

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

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

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

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added extra stats/visuals to drawing function.