Code for the simulations of the paper "Random forest weighted local Frechet regression with random objects"

The Frechet trees are constructed by the "FRFPackage" or "FRFPackage2" packages. The difference between the two is simply that the termination conditions for tree growth are different: the former is based on the number of split layers of the tree, and the latter is based on the maximum number of samples within a leaf. Our paper uses the former, and the corresponding code parameter is 'deep'. Later I realized that using the latter is more convenient for hyperparameter selection since its tuning range does not vary with sample size. The latter still use 'deep' to determine how deep trees will grow. As a rule of thumb, for the best performance, our local constant approach typically requires trees to be grown deeper, i.e., for the "FRFPackage" package, 'deep' often takes the maximum of the pre-given range; for the "FRFPackage2" package, 'deep' can take the value of 2. And our local linear method tends to favor medium-growth trees, for example, for the "FRFPackage2" package, 'deep ' often takes the value of 5. The code provided here uses the "FRFPackage2" packages. If you want to use the former, just replace all "FRFPackage2" packages in the simulation code (1-Distribution.R, 2-Covariance.R, 3-Sphere.R) with the "FRFPackage" packages.