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. (2019) <arXiv:1908.07963>. 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.

Version:	1.1.1
Depends:	R (≥ 4.0.0)
Imports:	cluster, matrixStats, nnet, seriation, stringdist, TraMineR, WeightedCluster
Suggests:	knitr, rmarkdown
Published:	2020-05-12
Author:	Keefe Murphy [aut, cre], Thomas Brendan Murphy [ctb], Raffaella Piccarreta [ctb], Isobel Claire Gormley [ctb]
Maintainer:	Keefe Murphy <keefe.murphy at ucd.ie>
BugReports:	https://github.com/Keefe-Murphy/MEDseq
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://cran.r-project.org/package=MEDseq
NeedsCompilation:	no
Citation:	MEDseq citation info
Materials:	README NEWS
CRAN checks:	MEDseq results

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