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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Penalized ECME Estimation for Censored Linear Mixed Models</dc:title>
  <dc:title>R package pecme version 0.1.1</dc:title>
  <dc:description>Fits Gaussian linear mixed models with a random intercept
    when the response is subject to left, right, and/or interval
    censoring, using the Expectation/Conditional Maximization Either
    (ECME) algorithm of Liu and Rubin (1994) in the spirit of the
    fast censored-response mixed-model algorithm of Vaida and Liu
    (2009). Simultaneous estimation and variable selection is
    supported through coordinate-descent penalized maximization with
    Lasso, Adaptive Lasso, SCAD, MCP, Elastic Net, and Ridge
    penalties (no penalty is also supported). The random intercept is
    integrated out by Gauss-Hermite quadrature at every iteration, and
    the two ECME conditional-maximization steps respectively maximize
    the expected penalized complete-data objective (for the
    regression coefficients) and the actual observed-data marginal
    likelihood (for the variance components), which is the defining
    feature of ECME relative to plain ECM/EM. The package provides a
    single-fit engine, a sequential/parallel penalty-parameter grid
    search with information-criterion or cross-validated selection,
    data-dependent or user-supplied lambda grids, and an
    Expectation-Maximization based treatment of a completely missing
    (at random) response, sharing the same truncated-normal machinery
    used for censoring. References: Liu and Rubin (1994)
    "The ECME algorithm: A simple extension of EM and ECM with
    faster monotone convergence" &lt;doi:10.1093/biomet/81.4.633&gt;;
    Vaida and Liu (2009) "Fast Implementation for Normal Mixed
    Effects Models With Censored Response"
    &lt;doi:10.1198/jcgs.2009.07130&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 4.1.0)</dc:relation>
  <dc:relation>Imports: stats, graphics, parallel, lme4, withr</dc:relation>
  <dc:relation>Suggests: testthat (&gt;= 3.0.0), waldo, mice</dc:relation>
  <dc:creator>Ali Asghar Haeri-Mehrizi &lt;haeri.stat@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Ali Asghar Haeri-Mehrizi [aut, cre],
  Arshia Haeri-Mehrizi [aut],
  Adel Mohammadpour [rev]</dc:contributor>
  <dc:rights>MIT + file LICENSE (https://CRAN.R-project.org/package=pecme/LICENSE)</dc:rights>
  <dc:date>2026-10-07</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=pecme</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.pecme</dc:identifier>
</oai_dc:dc>