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 ORCID iD [aut, cre, cph], David Benkeser ORCID iD [aut], Mark van der Laan ORCID iD [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


Reference manual: haldensify.pdf
Package source: haldensify_0.0.5.tar.gz
Windows binaries: r-devel: haldensify_0.0.5.zip, r-devel-gcc8: not available, r-release: haldensify_0.0.5.zip, r-oldrel: haldensify_0.0.5.zip
OS X binaries: r-release: haldensify_0.0.5.tgz, r-oldrel: haldensify_0.0.5.tgz


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