Package: haldensify
Title: Highly Adaptive Lasso Conditional Density Estimation
Version: 0.2.3
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 data augmentation 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, 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.
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
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
NeedsCompilation: no
Packaged: 2022-02-09 21:48:47 UTC; nsh
Author: Nima Hejazi [aut, cre, cph] (<https://orcid.org/0000-0002-7127-2789>),
  David Benkeser [aut] (<https://orcid.org/0000-0002-1019-8343>),
  Mark van der Laan [aut, ths] (<https://orcid.org/0000-0003-1432-5511>),
  Rachael Phillips [ctb] (<https://orcid.org/0000-0002-8474-591X>)
Repository: CRAN
Date/Publication: 2022-02-09 22:20:06 UTC
Built: R 4.2.0; ; 2023-07-11 00:52:01 UTC; unix
