kernel_regression: kernel regression estimator

 usage:
      fit = kernel_regression(eval_points, depvar, condvars, bandwidth)

 inputs:
      eval_points: PxK matrix of points at which to calculate the density
      depvar: Nx1 vector of observations of the dependent variable
      condvars: NxK matrix of data points
      bandwidth (optional): positive scalar, the smoothing parameter.
              Default is N ^ (-1/(4+K))
      kernel (optional): string. Name of the kernel function. Default is
              Gaussian kernel.
      prewhiten bool (optional): default true. If true, rotate data
              using Choleski decomposition of inverse of covariance,
              to approximate independence after the transformation, which
              makes a product kernel a reasonable choice.
      do_cv: bool (optional). default false. If true, calculate leave-1-out
               fit to calculate the cross validation score
      computenodes: int (optional, default 0).
              Number of compute nodes for parallel evaluation
      debug: bool (optional, default false). show results on compute nodes if doing
              a parallel run
 outputs:
      fit: Px1 vector: the fitted value at each of the P evaluation points.

Package: econometrics