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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 "proxymix" in publications use:'
type: software
license: MIT
title: 'proxymix: KL-Optimal Gaussian Mixture Proxies for Arbitrary Target Densities'
version: 0.3.0
identifiers:
- type: doi
  value: 10.32614/CRAN.package.proxymix
abstract: 'Fits multivariate Gaussian-mixture proxies that are Kullback-Leibler optimal
  to user-supplied target densities on real Euclidean space. Three fitting regimes
  are unified under one verb: (i) closed-form moment matching for a single component,
  (ii) classical expectation-maximisation when independent samples are available,
  and (iii) importance-sampled KLD-EM when the target can be evaluated point-wise
  but not (cheaply) sampled. Closed-form Gaussian-mixture operators (density, sampling,
  marginalisation, conditioning, divergence) round out the toolkit. Implements the
  regime hierarchy of Hoek and Elliott (2024) <https://doi.org/10.1080/07362994.2024.2372605>.'
authors:
- family-names: Moldovan
  given-names: Max
  email: max.moldovan@gmail.com
  orcid: https://orcid.org/0000-0001-9680-8474
preferred-citation:
  type: manual
  title: 'proxymix: KL-Optimal Gaussian Mixture Proxies for Arbitrary Target Densities'
  authors:
  - family-names: Moldovan
    given-names: Max
    email: max.moldovan@gmail.com
    orcid: https://orcid.org/0000-0001-9680-8474
  year: '2026'
  notes: R package version 0.1.1
repository: https://max578.r-universe.dev
repository-code: https://github.com/max578/proxymix
commit: 51d879b0d12f318671ddaa530d2b5cd1a20bcd97
url: https://github.com/max578/proxymix
date-released: '2026-06-02'
contact:
- family-names: Moldovan
  given-names: Max
  email: max.moldovan@gmail.com
  orcid: https://orcid.org/0000-0001-9680-8474
references:
- type: article
  title: Mixtures of multivariate Gaussians
  authors:
  - family-names: Hoek
    given-names: Johannes
    name-particle: van der
  - family-names: Elliott
    given-names: Robert J.
  journal: Stochastic Analysis and Applications
  year: '2024'
  doi: 10.1080/07362994.2024.2372605