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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 "mixsqp" in publications use:'
type: software
license: MIT
title: 'mixsqp: Sequential Quadratic Programming for Fast Maximum-Likelihood Estimation
  of Mixture Proportions'
version: 0.3-54
identifiers:
- type: doi
  value: 10.32614/CRAN.package.mixsqp
abstract: Provides an optimization method based on sequential quadratic programming
  (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture
  model where the component densities are known. The algorithm is expected to obtain
  solutions that are at least as accurate as the state-of-the-art MOSEK interior-point
  solver (called by function "KWDual" in the 'REBayes' package), and they are expected
  to arrive at solutions more quickly when the number of samples is large and the
  number of mixture components is not too large. This implements the "mix-SQP" algorithm,
  with some improvements, described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu
  (2020) <DOI:10.1080/10618600.2019.1689985>.
authors:
- family-names: Kim
  given-names: Youngseok
  email: youngseok@uchicago.edu
- family-names: Carbonetto
  given-names: Peter
  email: peter.carbonetto@gmail.com
- family-names: Anitescu
  given-names: Mihai
- family-names: Stephens
  given-names: Matthew
preferred-citation:
  type: article
  title: A fast algorithm for maximum likelihood estimation of mixture proportions
    using sequential quadratic programming
  authors:
  - name: Youngseok Kim
  - name: Peter Carbonetto
  - name: Matthew Stephens
  - name: Mihai Anitescu
  journal: Journal of Computational and Graphical Statistics
  volume: '29'
  issue: '2'
  year: '2020'
  url: https://doi.org/10.1080/10618600.2019.1689985
  start: '261'
  end: '273'
repository: https://stephenslab.r-universe.dev
repository-code: https://github.com/stephenslab/mixsqp
commit: 5146a57b2e2ae8a1ba2254bd8724998b87a92772
url: https://github.com/stephenslab/mixsqp
date-released: '2023-12-20'
contact:
- family-names: Carbonetto
  given-names: Peter
  email: peter.carbonetto@gmail.com