inst/reproducepointestimation/gandk_functions.R

In pierrejacob/winference: Approximate Bayesian Computation with the Wasserstein distance

library(Rcpp)

cppFunction("NumericVector gandkinversecdf_givennormals(NumericVector normals, NumericVector theta){
  NumericVector z = normals;
  double A = theta(0);
  double B = theta(1);
  double c = 0.8;
  double g = theta(2);
  double k = theta(3);
  return A + B * (1 + c * (1 - exp(- g * z)) / (1 + exp(- g * z))) * pow((1 + pow(z, 2.0)), k) * z;
}")

cppFunction("NumericVector gandkinversecdf(NumericVector x, NumericVector theta){
  NumericVector z = qnorm(x);
  double A = theta(0);
  double B = theta(1);
  double c = 0.8;
  double g = theta(2);
  double k = theta(3);
  return A + B * (1 + c * (1 - exp(- g * z)) / (1 + exp(- g * z))) * pow((1 + pow(z, 2.0)), k) * z;
}")

target <- list()

target$generate_randomness <- function(nobservations){
  return(rnorm(nobservations))
}

target$robservation <- function(theta, randomness){
  return(gandkinversecdf_givennormals(randomness, theta))
}

metricL1 = function(xvec,yvec)  mean(abs(xvec - yvec))