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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "haldensify" in publications use:'
type: software
license: MIT
title: 'haldensify: Highly Adaptive Lasso Conditional Density Estimation'
version: 0.2.6
doi: 10.5281/zenodo.3698329
identifiers:
- type: doi
  value: 10.32614/CRAN.package.haldensify
abstract: 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) <https://doi.org/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.
authors:
- family-names: Hejazi
  given-names: Nima
  email: nh@nimahejazi.org
  orcid: https://orcid.org/0000-0002-7127-2789
- family-names: Benkeser
  given-names: David
  email: benkeser@emory.edu
  orcid: https://orcid.org/0000-0002-1019-8343
- family-names: Laan
  given-names: Mark
  name-particle: van der
  email: laan@berkeley.edu
  orcid: https://orcid.org/0000-0003-1432-5511
preferred-citation:
  type: manual
  title: 'haldensify: Highly adaptive lasso conditional density estimation'
  authors:
  - family-names: Hejazi
    given-names: Nima S
  - family-names: Benkeser
    given-names: David
    email: benkeser@emory.edu
    orcid: https://orcid.org/0000-0002-1019-8343
  - family-names: Laan
    given-names: Mark J
    name-particle: van der
  year: '2024'
  notes: R package version 0.2.6
  doi: 10.5281/zenodo.3698329
  url: https://github.com/nhejazi/haldensify
repository: https://nhejazi.r-universe.dev
repository-code: https://github.com/nhejazi/haldensify
commit: 7a553e43ca64d97d29bcf0f5954470e8beeb8c20
url: https://github.com/nhejazi/haldensify
contact:
- family-names: Hejazi
  given-names: Nima
  email: nh@nimahejazi.org
  orcid: https://orcid.org/0000-0002-7127-2789
references:
- type: article
  title: 'haldensify: Highly adaptive lasso conditional density estimation in R'
  authors:
  - family-names: Hejazi
    given-names: Nima S
  - family-names: Laan
    given-names: Mark J
    name-particle: van der
  - family-names: Benkeser
    given-names: David
  year: '2022'
  journal: Journal of Open Source Software
  publisher:
    name: The Open Journal
  doi: 10.21105/joss.04522
  url: https://doi.org/10.21105/joss.04522
- type: article
  title: Efficient estimation of modified treatment policy effects based on the generalized
    propensity score
  authors:
  - family-names: Hejazi
    given-names: Nima S
  - family-names: Benkeser
    given-names: David
  - family-names: Díaz
    given-names: Iván
  - family-names: Laan
    given-names: Mark J
    name-particle: van der
  year: '2022'
  journal: arXiv
  url: https://arxiv.org/abs/2205.05777