Conditional density estimation is a longstanding and challenging
problem in statistical theory, and numerous proposals exist for optimally
estimating such complex functions. Algorithms for nonparametric estimation
of conditional densities based on a pooled hazard regression formulation and
semiparametric estimation via conditional hazards modeling are implemented
based on the highly adaptive lasso, a nonparametric regression function for
efficient estimation with fast convergence under mild assumptions. The
pooled hazards formulation implemented was first described by Díaz and
van der Laan (2011)