Conditional density estimation is a longstanding and challenging problem in statistical theory, and numerous proposals exist for optimally estimating such complex functions. An algorithm for nonparametric estimation of conditional densities based on a pooled hazard regression formulation is implemented based on the highly adaptive lasso (HAL), an optimal algorithm for efficient estimation with guarantees of fast convergence rates.




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