Package: MEDseq 1.4.2

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.

Authors:Keefe Murphy [aut, cre], Thomas Brendan Murphy [ctb], Raffaella Piccarreta [ctb], Isobel Claire Gormley [ctb]

MEDseq_1.4.2.tar.gz
MEDseq_1.4.2.zip(r-4.7)MEDseq_1.4.2.zip(r-4.6)MEDseq_1.4.2.zip(r-4.5)
MEDseq_1.4.2.tgz(r-4.6-any)MEDseq_1.4.2.tgz(r-4.5-any)
MEDseq_1.4.2.tar.gz(r-4.7-any)MEDseq_1.4.2.tar.gz(r-4.6-any)
MEDseq_1.4.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
MEDseq/json (API)
NEWS

# Install 'MEDseq' in R:
install.packages('MEDseq', repos = c('https://keefe-murphy.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/keefe-murphy/medseq/issues

Datasets:
  • biofam - Family life states from the Swiss Household Panel biographical survey
  • mvad - MVAD: Transition from school to work

On CRAN:

Conda:

distance-based-clusteringmixture-of-expertsmodel-based-clusteringsequence-analysis

4.80 score 5 stars 25 scripts 173 downloads 15 exports 47 dependencies

Last updated from:6db6a3ad82. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK161
source / vignettesOK188
linux-release-x86_64OK160
macos-release-arm64OK151
macos-oldrel-arm64OK134
windows-develOK124
windows-releaseOK116
windows-oldrelOK143
wasm-releaseOK120

Exports:dbsdist_freqwHget_MEDseq_resultsMEDseq_AvePPMEDseq_clustnamesMEDseq_compareMEDseq_controlMEDseq_entropyMEDseq_fitMEDseq_meantimeMEDseq_nameclustsMEDseq_newsMEDseq_stderrseqdefwKModes

Dependencies:bootcaclustercodetoolscolorspacedata.tabledigestdoFuturefastclusterforeachfuturefuture.applygclusglobalsiteratorslatticelistenvlme4marginsMASSMatrixmatrixStatsmgcvminqanlmenloptrnnetparallellypermutepredictionprogressrqaprbibutilsRColorBrewerRcppRcppEigenRdpackreformulasregistryrlangseriationstringdistTraMineRTSPveganvegclustWeightedCluster

MEDseq: Mixtures of Exponential-Distance Models with Covariates

Keefe Murphy

Rendered fromMEDseq.Rmdusingknitr::rmarkdownon Jun 03 2026.

Last update: 2024-06-14
Started: 2019-08-23