Dirac Delta Regression (DDR)

This is an R package implementing DDR, an algorithm that transforms the response variable of regression into a set of asymptotically Dirac delta functions using kernel density functions. This allows the user to convert a non-linear regressor to a conditional density estimator. We use kernel ridge regression with a Gaussian reproducing kernel as the underlying regressor in this implementation.


Please install the FNN, pracma, doParallel and foreach packages on CRAN. Then:




numCores <- detectCores()-1; registerDoParallel(numCores) # set up parallel computing

cd_est = DDR(matrix(rnorm(400),200,2),rnorm(200),matrix(rnorm(20),10,2)) # run DDR

plot(cd_est$y,cd_est$dens[1,],type="l") # plot the conditional density estimate of the first test sample

ericstrobl/DDR documentation built on April 5, 2020, 5:34 p.m.