haldensify: Highly Adaptive Lasso Conditional Density Estimation

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>.

Package details

AuthorNima 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>)
MaintainerNima Hejazi <nh@nimahejazi.org>
LicenseMIT + file LICENSE
Version0.0.6
URL https://github.com/nhejazi/haldensify
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("haldensify")

Try the haldensify package in your browser

Any scripts or data that you put into this service are public.

haldensify documentation built on Sept. 16, 2020, 9:07 a.m.