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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 "fmrs" in publications use:'
type: software
license: GPL-3.0-only
title: 'fmrs: Variable Selection in Finite Mixture of AFT Regression and FMR Models'
version: 1.22.0
identifiers:
- type: doi
  value: 10.32614/CRAN.package.fmrs
abstract: The package obtains parameter estimation, i.e., maximum likelihood estimators
  (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of
  Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture
  of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with
  Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson
  algorithm (for Weibull distribution). More importantly, the package obtains the
  maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively
  called FMRs). A component-wise tuning parameter selection based on a component-wise
  BIC is implemented in the package. Furthermore, this package provides Ridge Regression
  and Elastic Net.
authors:
- family-names: Shokoohi
  given-names: Farhad
  email: shokoohi@icloud.com
  orcid: https://orcid.org/0000-0002-6224-2609
preferred-citation:
  type: manual
  title: 'fmrs: Variable Selection in Finite Mixture of AFT Regression and FMR, Version
    0.99.1'
  authors:
  - family-names: Shokoohi
    given-names: Farhad
    email: shokoohi@icloud.com
    orcid: https://orcid.org/0000-0002-6224-2609
  - family-names: Khalili
    given-names: Abbas
  - family-names: Asgharian
    given-names: Masoud
  - family-names: Lin
    given-names: Shili
  year: '2020'
  url: https://github.com/shokoohi/fmrs
repository: https://bioc-release.r-universe.dev
repository-code: https://github.com/shokoohi/fmrs
commit: 661ad16a6a4519161824b82cbfeea02f7270147d
url: https://github.com/shokoohi/fmrs
date-released: '2023-05-16'
contact:
- family-names: Shokoohi
  given-names: Farhad
  email: shokoohi@icloud.com
  orcid: https://orcid.org/0000-0002-6224-2609
references:
- type: article
  title: Capturing heterogeneity of covariate effects in hidden subpopulations in
    the presence of censoring and large number of covariates
  authors:
  - family-names: Shokoohi
    given-names: Farhad
  - family-names: Khalili
    given-names: Abbas
  - family-names: Asgharian
    given-names: Masoud
  - family-names: Lin
    given-names: Shili
  year: '2019'
  journal: The Annals of Applied Statistics
  volume: '13'
  issue: '1'
  start: '444'
  end: '465'