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# CITATION.cff 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 "tiltdens" in publications use:'
type: software
license: MIT
title: 'tiltdens: Tilted and Data-Sharpened Nonparametric Density Estimation'
version: 0.2.1
doi: 10.32614/CRAN.package.tiltdens
abstract: 'High-order nonparametric density estimators built by perturbing a conventional
  kernel estimator, either by re-weighting the observations ("tilting") or by moving
  them ("data sharpening"). The perturbation is chosen so that the estimator inherits
  the fast convergence rate of an infinite-order kernel estimator, such as the sinc
  or trapezoidal flat-top estimator, while remaining a proper non-negative density
  without the oscillatory tails those estimators suffer from. Two criteria are provided:
  minimising the L2 distance to an infinite-order comparator, following Doosti and
  Hall (2016) <https://doi.org/10.1111/rssb.12112>, and minimising a cross-validation
  criterion that needs no comparator and is much faster, following Doosti, Hall and
  Mateu (2018) <https://doi.org/10.1016/j.jspi.2017.12.003>.'
authors:
- family-names: Doosti
  given-names: Hassan
  email: hassan.doosti@mq.edu.au
repository: https://doostih.r-universe.dev
repository-code: https://github.com/DoostiH/tiltdens
commit: bc1cc9d1e91bc2cd9e0862632865d5201bf865d3
url: https://github.com/DoostiH/tiltdens
date-released: '2026-10-02'
contact:
- family-names: Doosti
  given-names: Hassan
  email: hassan.doosti@mq.edu.au