Package: haldensify
Title: Highly Adaptive Lasso Conditional Density Estimation
Version: 0.2.5
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 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 formulation implemented was first described
    by Díaz and van der Laan (2011) <doi:10.2202/1557-4679.1356>. To complement
    the conditional density estimation utilities, nonparametric inverse
    probability weighted (IPW) estimators of the causal effects of additive
    modified treatment policies are implemented, using the conditional density
    estimation procedure to estimate the generalized propensity score. Per
    Hejazi, Benkeser, Díaz, and van der Laan <>10.48550/arXiv.2205.05777>,
    these nonparametric IPW estimators can be coupled with sieve estimation
    (undersmoothing) of the generalized propensity score estimators to attain
    the non/semi-parametric efficiency bound.
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),
    stringr,
    rlang,
    scales,
    Rdpack
Suggests:
    testthat,
    knitr,
    rmarkdown,
    covr,
    future
License: MIT + file LICENSE
URL: https://github.com/nhejazi/haldensify
BugReports: https://github.com/nhejazi/haldensify/issues
Encoding: UTF-8
VignetteBuilder: knitr
RoxygenNote: 7.1.2
RdMacros: Rdpack
