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: |
0.0.6 |
| Depends: |
R (≥ 3.2.0) |
| Imports: |
stats, ggplot2, data.table, matrixStats, future.apply, assertthat, hal9001 (≥ 0.2.5), origami (≥ 1.0.3), Rdpack |
| Suggests: |
testthat, knitr, rmarkdown, future, dplyr |
| Published: |
2020-09-16 |
| 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 |