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[ascl:2005.010]
NNKCDE: Nearest Neighbor Kernel Conditional Density Estimation

NNKCDE is a simple and easily interpretable Conditional Density Estimation (CDE) method. It computes a kernel density estimate of y using the k nearest neighbors of the evaluation point x. The model has only two tuning parameters: the number of nearest neighbors k and the bandwidth h of the smoothing kernel in y-space. Both tuning parameters are chosen in a principled way by minimizing the CDE loss on validation data.

[ascl:2005.018]
RFCDE: Random Forests for Conditional Density Estimation

RFCDE provides an implementation of random forests designed for conditional density estimation. It computes a kernel density estimate of y with nearest neighbor weightings defined by the location of the evaluation point x relative to the leaves in the random forest.