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haldensify: Highly Adaptive Lasso Conditional Density Estimation

Hejazi, Nima S; Benkeser, David C; van der Laan, Mark J

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) <https://doi.org/10.2202/1557-4679.1356>.

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