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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>Hidden Markov Models and Shifted Compound Poisson-Gamma Renewal
Process</dc:title>
  <dc:title>R package HMMscpg version 1.0.0</dc:title>
  <dc:description>Provides functions to define, simulate and fit hidden Markov models,
    hidden semi-Markov models, and their time-varying versions, to incompletely
    observed point process records. Such data arise when some events of an
    underlying point process are not recorded or are otherwise missing. The
    models treat the number of missing events between each pair of consecutively
    observed events of a point process record as a hidden process. The number of
    missing events may be fixed or modelled as a random variable whose
    distribution can vary over time. When the number of missing events is fixed,
    the observed event times can be modelled as a compound gamma renewal process.
    When it is random, the observed process is represented by a shifted compound
    Poisson-gamma renewal process, which may also depend on time, leading to a
    time-dependent shifted compound Poisson-gamma renewal process. These
    modelling frameworks are described in detail in Shahzadi, Wang, Parry and
    Bebbington (2023) &lt;doi:10.1007/s10463-022-00843-5&gt; and Shahzadi, Wang, Parry
    and Bebbington (2026) &lt;doi:10.1007/s11634-025-00632-x&gt;. The model parameters
    are estimated through direct nonlinear numerical optimization of the
    corresponding likelihood function, and each fit reports whether the change
    points of its time-dependent components are identified by the data. The
    package also provides methods for assessing model adequacy through residual
    analysis. In addition, it calculates and plots the most probable sequences of
    hidden states, allowing users to examine changes in the underlying
    missing-event process over time.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Imports: Rdpack, methods, stats, utils, Bessel, Rcpp</dc:relation>
  <dc:relation>LinkingTo: Rcpp, RcppArmadillo</dc:relation>
  <dc:relation>Suggests: knitr, rmarkdown, Rmpfr</dc:relation>
  <dc:creator>Amina Shahzadi &lt;aminashahzadi@gcu.edu.pk&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Amina Shahzadi [aut, cre],
  Ting Wang [aut]</dc:contributor>
  <dc:rights>GPL-3</dc:rights>
  <dc:date>2026-10-28</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=HMMscpg</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.HMMscpg</dc:identifier>
</oai_dc:dc>