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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 "tseLCA" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'tseLCA: Three-Step Estimation for Latent Class Analysis'
version: 1.0.2
abstract: Implements BCH (Bolck-Croon-Hagenaars) <https://doi.org/10.1093/pan/mph001>
  and ML (Vermunt's maximum likelihood) <https://doi.org/10.1093/pan/mpq025> approaches
  for three-step estimation of latent class models with covariates and distal outcomes,
  following Bakk, Tekle & Vermunt (2013) <https://doi.org/10.1177/0081175012470644>,
  Bakk, Oberski & Vermunt (2014) <https://www.jstor.org/stable/24573086>, and Bakk
  & Kuha (2018) <https://doi.org/10.1007/s11336-017-9592-7>. Built on 'multilevLCA'
  (Lyrvall et al., 2025) <https://doi.org/10.1080/00273171.2025.2473935> for Step-1
  measurement model estimation, this package extends it with support for Gaussian,
  Poisson, and binomial distal outcome families. Unlike 'poLCA', which relies on one-step
  estimation and cannot accommodate a measurement model from a different sample, this
  package uses a stepwise approach to prevent the structural model from influencing
  latent class formation. Implements correct sandwich variance estimation that propagates
  measurement uncertainty from the first-step through classification-error correction
  in the final step (Bakk, Oberski & Vermunt, 2014). Supports polytomous items and
  missing data in the measurement model with full information maximum likelihood.
  A data-generating process replicating the Bakk & Kuha (2018) simulation study is
  included.
authors:
- family-names: Lee
  given-names: Sam
  email: samlee@arizona.edu
  orcid: https://orcid.org/0009-0007-2977-6661
preferred-citation:
  type: manual
  title: 'tseLCA: Three-Step Estimation for Latent Class Analysis'
  authors:
  - family-names: Lee
    given-names: Sam
    email: samlee@arizona.edu
    orcid: https://orcid.org/0009-0007-2977-6661
  year: '2026'
  notes: R package version 1.0.0
  url: https://github.com/SamLeeBYU/tseLCA
repository: https://cran.r-universe.dev
repository-code: https://github.com/SamLeeBYU/tseLCA
commit: df20b56326120c7d1664fcbee96a52cc3034e313
url: https://samleebyu.github.io/tseLCA/
date-released: '2026-07-11'
contact:
- family-names: Lee
  given-names: Sam
  email: samlee@arizona.edu
  orcid: https://orcid.org/0009-0007-2977-6661
references:
- type: article
  title: Estimating the Association Between Latent Class Membership and External Variables
    Using Bias-Adjusted Three-step Approaches
  authors:
  - family-names: Bakk
    given-names: Zsuzsa
  - family-names: Tekle
    given-names: Fetene B.
  - family-names: Vermunt
    given-names: Jeroen K.
  journal: Sociological Methodology
  year: '2013'
  volume: '43'
  issue: '1'
  doi: 10.1177/0081175012470644
  start: '272'
  end: '311'
- type: article
  title: 'Relating Latent Class Assignments to External Variables: Standard Errors
    for Correct Inference'
  authors:
  - family-names: Bakk
    given-names: Zsuzsa
  - family-names: Oberski
    given-names: Daniel L.
  - family-names: Vermunt
    given-names: Jeroen K.
  journal: Political Analysis
  year: '2014'
  volume: '22'
  issue: '4'
  url: http://www.jstor.org/stable/24573086
  start: '520'
  end: '540'
- type: article
  title: Two-Step Estimation of Models Between Latent Classes and External Variables
  authors:
  - family-names: Bakk
    given-names: Zsuzsa
  - family-names: Kuha
    given-names: Jouni
  journal: Psychometrika
  year: '2018'
  volume: '83'
  issue: '4'
  doi: 10.1007/s11336-017-9592-7
  start: '871'
  end: '892'
- type: article
  title: 'Multilevel Latent Class Analysis: State-of-the-Art Methodologies and Their
    Implementation in the R Package multilevLCA'
  authors:
  - family-names: Lyrvall
    given-names: Johan
  - family-names: Di Mari
    given-names: Roberto
  - family-names: Bakk
    given-names: Zsuzsa
  - family-names: Oser
    given-names: Jennifer
  - family-names: Kuha
    given-names: Jouni
  journal: Multivariate Behavioral Research
  year: '2025'
  volume: '60'
  issue: '4'
  doi: 10.1080/00273171.2025.2473935
  start: 731-747