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@vucko gave me excellent answer on my question, unfortunately using Mathematica code. I'm trying to rewrite it in R and I'm lost in R functions providing kernel density estimation.

I have bivariate dataset with more than 46,000 rows (so I am also looking for a high performance solution--@vucko's solution is very time consuming). I would like to apply kernel density estimation and decide if some point lies in area with some density estimation level (confidence level respectively).

@vucko in his answer selected two groups. I need only to know if some point lies in the green group or not. And that should be done with R.

I experimented with kde and bkde2D functions but they don't provide me desired functionality as Mathematica SmoothKernelDistribution. Can you please me show the direction? For normal distribution I found the ellipse function which approximated data with some confidence level and used inside.owin function.

@vucko gave me excellent answer on my question, unfortunately using Mathematica code. I'm trying to rewrite it in R and I'm lost in R functions providing kernel density estimation.

I have bivariate dataset with more than 46,000 rows (so I am also looking for a high performance solution--@vucko's solution is very time consuming). I would like to apply kernel density estimation and decide if some point lies in area with some density estimation level (confidence level respectively).

@vucko in his answer selected two groups. I need only to know if some point lies in the green group or not. And that should be done with R.

I experimented with kde and bkde2D functions but they don't provide me desired functionality as Mathematica SmoothKernelDistribution. Can you please me show the direction? For normal distribution I found the ellipse function which approximated data with some confidence level and used inside.owin function.

@vucko gave me excelent aswer on my question. Unfortunately in Mathematica code. I'm trying to rewrite it in R and I'm lost in R functions providing Kernel density estimation.

I have bivariate dataset with more than 46000 rows (so I need also high performance solution, vucko's solution is very time consuming), I would like to apply kernel density estimation and decide if some point lies in area with some density estimation level (confidence level respectively).

Vucko in his answer selects two groups. I need only know if some point lies in the green group or not. And done it with R.

I experimented with kde and bkde2D functions but they don't provide me desired functionality as Mathematica SmoothKernelDistribution. Can you please me show the direction? For normal distribution I found ellipse function which approximated data with some confidence level and used inside.owin function...

@vucko gave me excellent answer on my question, unfortunately using Mathematica code. I'm trying to rewrite it in R and I'm lost in R functions providing kernel density estimation.

I have bivariate dataset with more than 46,000 rows (so I am also looking for a high performance solution--@vucko's solution is very time consuming). I would like to apply kernel density estimation and decide if some point lies in area with some density estimation level (confidence level respectively).

@vucko in his answer selected two groups. I need only to know if some point lies in the green group or not. And that should be done with R.

I experimented with kde and bkde2D functions but they don't provide me desired functionality as Mathematica SmoothKernelDistribution. Can you please me show the direction? For normal distribution I found the ellipse function which approximated data with some confidence level and used inside.owin function.