Performs (Adaptive) Boosting Trees for Poisson distributed response variables, using log-link function.
  The code approach is similar to the one used in 'gbm'/'gbm3'. Moreover, each tree in the expansion is built thanks to the 'rpart' package.
  This package is based on following books and articles
  Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-25820-7>
  Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-57556-4>
  Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-25827-6>
  Denuit, M., Hainaut, D., Trufin, J. (2022) <doi:10.1080/03461238.2022.2037016>
  Denuit, M., Huyghe, J., Trufin, J. (2022) <https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A244325/datastream/PDF_01/view>
  Denuit, M., Trufin, J., Verdebout, T. (2022) <https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A268577>.

The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. 
        The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid.
        The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid.
        The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid.
        The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid.
        Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>, 
        Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.