Package: MEDseq
Type: Package
Date: 2021-12-19
Title: Mixtures of Exponential-Distance Models with Covariates
Version: 1.3.2
Authors@R: c(person("Keefe", "Murphy", email = "keefe.murphy@mu.ie", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7709-3159")),
           person("Thomas Brendan", "Murphy", email = "brendan.murphy@ucd.ie", role = "ctb", comment = c(ORCID = "0000-0002-5668-7046")),
           person("Raffaella", "Piccarreta", email = "raffaella.piccarreta@unibocconi.it ", role = "ctb"),
           person("Isobel Claire", "Gormley", email = "claire.gormley@ucd.ie", role = "ctb", comment = c(ORCID = "0000-0001-7713-681X")))
Description: Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics. Sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.
Depends: R (>= 4.0.0)
License: GPL (>= 2)
Encoding: UTF-8
URL: https://cran.r-project.org/package=MEDseq
BugReports: https://github.com/Keefe-Murphy/MEDseq
LazyData: true
Imports: cluster, matrixStats (>= 0.53.1), nnet (>= 7.3-0), seriation,
        stringdist, TraMineR (>= 1.6), WeightedCluster
Suggests: knitr, rmarkdown, viridisLite (>= 0.4.0)
RoxygenNote: 7.1.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-12-19 11:02:43 UTC; Keefe
Author: Keefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>),
  Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>),
  Raffaella Piccarreta [ctb],
  Isobel Claire Gormley [ctb] (<https://orcid.org/0000-0001-7713-681X>)
Maintainer: Keefe Murphy <keefe.murphy@mu.ie>
Repository: CRAN
Date/Publication: 2021-12-19 11:32:03 UTC
