title: Finding a density peak / cluster centrum in 2D grid / point process
body: <p>I have a dataset with minute by minute GPS coordinates recorded by a persons cellphone. I.e. the dataset has 1440 rows with LON/LAT values. Based on the data I would like a point estimate  (lon/lat value) of where the participants home is. Let's assume that home is the single location where they spend most of their time in a given 24h interval. Furthermore, the GPS sensor most of the time has quite high accuracy, however sometimes it is completely off resulting in gigantic outliers.</p>
  
  <p>I think the best way to go about this is to treat it as a point process and use 2D density estimation to find the peak. Is there a native way to do this in R? I looked into kde2d (MASS) but this didn't really seem to do the trick. Kde2d creates a 25x25 grid of the data range with density values. However, in my data, the person can easily travel 100 miles or more per day, so these blocks are generally too large of an estimate. I could narrow them down and use a much larger grid but I am sure there must be a better way to get a point estimate. </p>