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>.

Version:	0.0.5
Depends:	R (≥ 3.2.0)
Imports:	stats, ggplot2, data.table, future.apply, assertthat, hal9001 (≥ 0.2.5), origami (≥ 1.0.0), Rdpack
Suggests:	testthat, knitr, rmarkdown, future, dplyr
Published:	2020-03-14
Author:	Nima Hejazi [aut, cre, cph], David Benkeser [aut], Mark van der Laan [aut, ths]
Maintainer:	Nima Hejazi <nh at nimahejazi.org>
BugReports:	https://github.com/nhejazi/haldensify/issues
License:	MIT + file LICENSE
URL:	https://github.com/nhejazi/haldensify
NeedsCompilation:	no
Citation:	haldensify citation info
Materials:	README NEWS
CRAN checks:	haldensify results

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