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 flexible estimation of the conditional density, the highly
adaptive lasso, a non-parametric regression function shown to estimate
cadlag (RCLL) functions at a suitably fast convergence rate, is used. The
use of pooled hazards regression for conditional density estimation as
implemented here was first described for by Díaz and van der Laan (2011)
<doi:10.2202/1557-4679.1356>. Building on the conditional density estimation
utilities, non-parametric inverse probability weighted (IPW) estimators of
the causal effects of additive modified treatment policies are implemented,
using conditional density estimation to estimate the generalized propensity
score. Non-parametric IPW estimators based on this can be coupled with
undersmoothing of the generalized propensity score estimator to attain the
semi-parametric efficiency bound (per Hejazi, Díaz, and van der Laan
<doi:10.48550/arXiv.2205.05777>).
Version: |
0.2.8 |
Depends: |
R (≥ 3.2.0) |
Imports: |
stats, utils, dplyr, tibble, ggplot2, data.table, matrixStats, future.apply, assertthat, hal9001 (≥ 0.4.6), origami (≥
1.0.7), stringr, rlang, scales, Rdpack |
Suggests: |
testthat, knitr, rmarkdown, covr, future |
Published: |
2025-09-02 |
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://codex.nimahejazi.org/haldensify/ |
NeedsCompilation: |
no |
Citation: |
haldensify citation info |
Materials: |
README, NEWS |
CRAN checks: |
haldensify results [issues need fixing before 2025-09-30] |
Documentation:
Downloads:
Reverse dependencies:
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