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) <doi:10.2202/1557-4679.1356>.
|Author||Nima Hejazi [aut, cre, cph] (<https://orcid.org/0000-0002-7127-2789>), David Benkeser [aut] (<https://orcid.org/0000-0002-1019-8343>), Mark van der Laan [aut, ths] (<https://orcid.org/0000-0003-1432-5511>)|
|Maintainer||Nima Hejazi <firstname.lastname@example.org>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.