Package: haldensify
Title: Highly Adaptive Lasso Conditional Density Estimation
Version: 0.0.5
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"))
  )
Maintainer: Nima Hejazi <nh@nimahejazi.org>
Description: Conditional density estimation is a longstanding and challenging
    problem in statistical theory, and numerous proposals exist for optimally
    estimating such complex functions. Algorithms for nonparametric estimation
    of conditional densities based on a pooled hazard regression formulation and
    semiparametric estimation via conditional hazards modeling are implemented
    based on the highly adaptive lasso, a nonparametric regression function for
    efficient estimation with fast convergence under mild assumptions. The
    pooled hazards formulation implemented was first described by Díaz and
    van der Laan (2011) <doi:10.2202/1557-4679.1356>.
Depends: R (>= 3.2.0)
Imports: stats, ggplot2, data.table, future.apply, assertthat, hal9001
        (>= 0.2.5), origami (>= 1.0.0), Rdpack
Suggests: testthat, knitr, rmarkdown, future, dplyr
License: MIT + file LICENSE
URL: https://github.com/nhejazi/haldensify
BugReports: https://github.com/nhejazi/haldensify/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2020-03-06 06:00:01 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>)
Repository: CRAN
Date/Publication: 2020-03-14 15:20:05 UTC
