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
Config/pak/sysreqs: libicu-dev
Repository: https://nhejazi.r-universe.dev
Date/Publication: 2026-05-12 20:29:11 UTC
RemoteUrl: https://github.com/nhejazi/haldensify
RemoteRef: HEAD
RemoteSha: e4115e91322db1e71f6cc55717c6733ce58b260f
NeedsCompilation: no
Packaged: 2026-06-11 06:29:56 UTC; root
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>)