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 |
| 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 + fileLICENSE |
| URL: | https://codex.nimahejazi.org/haldensify/ |
| NeedsCompilation: | no |
| Citation: | haldensify citation info |
| Materials: | README,NEWS |
| CRAN checks: | haldensify results |
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