Package: haldensify
Title: Highly Adaptive Lasso Conditional Density Estimation
Version: 0.2.8
Authors@R: c(
    person("Nima", "Hejazi", email = "nh@nimahejazi.org",
           role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0002-7127-2789")),
    person("David", "Benkeser", email = "benkeser@emory.edu",
           role = "aut",
           comment = c(ORCID = "0000-0002-1019-8343")),
    person("Mark", "van der Laan", email = "laan@berkeley.edu",
           role = c("aut", "ths"),
           comment = c(ORCID = "0000-0003-1432-5511")),
    person("Rachael", "Phillips", email = "rachaelvphillips@berkeley.edu",
           role = "ctb",
           comment = c(ORCID = "0000-0002-8474-591X"))
  )
Maintainer: Nima Hejazi <nh@nimahejazi.org>
Description: 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>).
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
License: MIT + file LICENSE
URL: https://codex.nimahejazi.org/haldensify/
BugReports: https://github.com/nhejazi/haldensify/issues
Encoding: UTF-8
VignetteBuilder: knitr
RoxygenNote: 7.3.2
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2025-09-01 19:24:13 UTC; nih914
Author: Nima Hejazi [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0002-7127-2789>),
  David Benkeser [aut] (ORCID: <https://orcid.org/0000-0002-1019-8343>),
  Mark van der Laan [aut, ths] (ORCID:
    <https://orcid.org/0000-0003-1432-5511>),
  Rachael Phillips [ctb] (ORCID: <https://orcid.org/0000-0002-8474-591X>)
Repository: CRAN
Date/Publication: 2025-09-02 07:40:06 UTC
Built: R 4.6.0; ; 2026-04-25 20:24:05 UTC; unix
