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 DÃaz 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 [issues need fixing before 2025-09-01] Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=haldensify to link to this page.
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