haldensify: Highly Adaptive Lasso Conditional Density Estimation
An algorithm for flexible conditional density estimation based on
application of pooled hazard regression to an artificial repeated measures
dataset constructed by discretizing the support of the outcome variable. To
facilitate non/semi-parametric estimation of the conditional density, the
highly adaptive lasso, a nonparametric regression function shown to reliably
estimate a large class of functions at a fast convergence rate, is utilized.
The pooled hazards data augmentation formulation implemented was first
described by Díaz and van der Laan (2011) <doi:10.2202/1557-4679.1356>. To
complement the conditional density estimation utilities, tools for efficient
nonparametric inverse probability weighted (IPW) estimation of the causal
effects of stochastic shift interventions (modified treatment policies),
directly utilizing the density estimation technique for construction of the
generalized propensity score, are provided. These IPW estimators utilize
undersmoothing (sieve estimation) of the conditional density estimators in
order to achieve the non/semi-parametric efficiency bound.
Version: |
0.2.3 |
Depends: |
R (≥ 3.2.0) |
Imports: |
stats, utils, dplyr, tibble, ggplot2, data.table, matrixStats, future.apply, assertthat, hal9001 (≥ 0.4.1), origami (≥
1.0.3), rsample, rlang, scales, Rdpack |
Suggests: |
testthat, knitr, rmarkdown, stringr, covr, future |
Published: |
2022-02-09 |
DOI: |
10.32614/CRAN.package.haldensify |
Author: |
Nima Hejazi [aut,
cre, cph],
David Benkeser
[aut],
Mark van der Laan
[aut, ths],
Rachael Phillips
[ctb] |
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 |
Documentation:
Downloads:
Reverse dependencies:
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