  Random forests is a common non-parametric regression technique which performs
well for mixed-type data and irrelevant covariates, while being robust to
monotonic variable transformations. Existing random forest implementations
target regression or classification. We introduce the RFCDE package for fitting
random forest models optimized for nonparametric conditional density
estimation, including joint densities for multiple responses. This enables
analysis of conditional probability distributions which is useful for
propagating uncertainty and of joint distributions that describe relationships
between multiple responses and covariates. RFCDE is released under the MIT
open-source license and can be accessed at https://github.com/tpospisi/rfcde .
Both R and Python versions, which call a common C++ library, are available.
