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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 "essHist" in publications use:'
type: software
license: GPL-3.0-only
title: 'essHist: The Essential Histogram'
version: 1.2.2
doi: 10.32614/CRAN.package.essHist
abstract: Provide an optimal histogram, in the sense of probability density estimation
  and features detection, by means of multiscale variational inference. In other words,
  the resulting histogram servers as an optimal density estimator, and meanwhile recovers
  the features, such as increases or modes, with both false positive and false negative
  controls. Moreover, it provides a parsimonious representation in terms of the number
  of blocks, which simplifies data interpretation. The only assumption for the method
  is that data points are independent and identically distributed, so it applies to
  fairly general situations, including continuous distributions, discrete distributions,
  and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.
authors:
- family-names: Li
  given-names: Housen
  email: housen.li@outlook.com
- family-names: Sieling
  given-names: Hannes
  email: hsielin@uni-goettingen.de
repository: https://housenli.r-universe.dev
commit: ddbbb812f2802869139c53e36fa304d3b93ba331
date-released: '2019-05-10'
contact:
- family-names: Li
  given-names: Housen
  email: housen.li@outlook.com