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:

Notes de version:

added quartic and triweight kernels.

Notes de version:

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

Notes de version:

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

Notes de version:

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

Notes de version:

added extra stats/visuals to drawing function.