kde2D

N by 2 matrix with the two variables as columns

data

size of the n by n grid over which the density is computed

limits of the bounding box over which the density is computed;
format: c(lower_Xlim, upper_Xlim, lower_Ylim, upper_Ylim)

limits

The kernel is assumed to be Gaussian. Bandwidth matrix is
 diagonal. The two bandwidth parameters are chosen optimally
 without ever using/assuming any parametric model for the data
 or any "rules of thumb". Unlike many other procedures, this one
 is immune to accuracy failures in the estimation of multimodal
 densities with widely separated modes. This function in meant to be
 the R implementation of the MATLAB <code>kde2d()</code> function written 
 and published by Z. I. Botev at:
 <a href="http://web.maths.unsw.edu.au/~zdravkobotev/">http://web.maths.unsw.edu.au/~zdravkobotev/</a>

Dynamic panel modelling framework based on an empirical-Bayes approach.
Contains tools for computing point forecasts and bootstrapping prediction intervals.
Reference: Liu et al. (2016) <doi:10.2139/ssrn.2889000>.

Michal Oleszak

pmpp

Posterior Mean Panel Predictor

kde2D function

The kernel is assumed to be Gaussian. Bandwidth matrix is
 diagonal. The two bandwidth parameters are chosen optimally
 without ever using/assuming any parametric model for the data
 or any "rules of thumb". Unlike many other procedures, this one
 is immune to accuracy failures in the estimation of multimodal
 densities with widely separated modes. This function in meant to be
 the R implementation of the MATLAB <code>kde2d()</code> function written 
 and published by Z. I. Botev at:
 <a href='http://web.maths.unsw.edu.au/~zdravkobotev/'>http://web.maths.unsw.edu.au/~zdravkobotev/</a>

kde2D: Compute a two-dimensional kernel density estimate

Description

Usage

Arguments

Value

References