MEDseq: Mixtures of Exponential-Distance Models with Covariates

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 and sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.

Version: 1.4.1
Depends: R (≥ 4.0.0)
Imports: cluster, matrixStats (≥ 1.0.0), nnet (≥ 7.3-0), seriation, stringdist, TraMineR (≥ 1.6), WeightedCluster
Suggests: knitr, rmarkdown, viridisLite (≥ 0.4.0)
Published: 2023-12-12
DOI: 10.32614/CRAN.package.MEDseq
Author: Keefe Murphy ORCID iD [aut, cre], Thomas Brendan Murphy ORCID iD [ctb], Raffaella Piccarreta [ctb], Isobel Claire Gormley ORCID iD [ctb]
Maintainer: Keefe Murphy <keefe.murphy at>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: MEDseq citation info
Materials: README NEWS
CRAN checks: MEDseq results


Reference manual: MEDseq.pdf
Vignettes: MEDseq


Package source: MEDseq_1.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): MEDseq_1.4.1.tgz, r-oldrel (arm64): MEDseq_1.4.1.tgz, r-release (x86_64): MEDseq_1.4.1.tgz, r-oldrel (x86_64): MEDseq_1.4.1.tgz
Old sources: MEDseq archive


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