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✨
DESCRIPTION |NEWS
card.svg |card.png
MEDseq/json (API)

# 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:

distance-based-clustering mixture-of-experts model-based-clustering sequence-analysis

4.81 score 5 stars 26 scripts 344 downloads 15 exports 47 dependencies

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

Target	Result	Time
linux-devel-x86_64	OK	187
source / vignettes	OK	200
linux-release-x86_64	OK	180
macos-release-arm64	OK	110
macos-oldrel-arm64	OK	90
windows-devel	OK	110
windows-release	OK	127
windows-oldrel	OK	128
wasm-release	OK	164

Exports:dbs dist_freqwH get_MEDseq_results MEDseq_AvePP MEDseq_clustnames MEDseq_compare MEDseq_control MEDseq_entropy MEDseq_fit MEDseq_meantime MEDseq_nameclusts MEDseq_news MEDseq_stderr seqdef wKModes

Dependencies:boot ca cluster codetools colorspace data.table digest doFuture fastcluster foreach future future.apply gclus globals iterators lattice listenv lme4 margins MASS Matrix matrixStats mgcv minqa nlme nloptr nnet parallelly permute prediction progressr qap rbibutils RColorBrewer Rcpp RcppEigen Rdpack reformulas registry rlang seriation stringdist TraMineR TSP vegan vegclust WeightedCluster

MEDseq: Mixtures of Exponential-Distance Models with Covariates

Introduction | Installing MEDseq | MVAD Data | Biofam Data | References

Rendered fromMEDseq.Rmdusingknitr::rmarkdown

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

Help page	Topics
MEDseq: Mixtures of Exponential-Distance Models with Covariates	MEDseq-package MEDseq
Family life states from the Swiss Household Panel biographical survey	biofam
Compute the Density-based Silhouette	dbs
Pairwise frequency-Weighted Hamming distance matrix for categorical data	dist_freqwH
Extract results from a MEDseq model	get_MEDseq_results
Average posterior probabilities of a fitted MEDseq model	MEDseq_AvePP
Automatic labelling of clusters using central sequences	MEDseq_clustnames MEDseq_nameclusts
Choose the best MEDseq model	MEDseq_compare print.MEDseqCompare
Set control values for use with MEDseq_fit	MEDseq_control
Entropy of a fitted MEDseq model	MEDseq_entropy
MEDseq: Mixtures of Exponential-Distance Models with Covariates	MEDseq_fit print.MEDseq summary.MEDseq
Compute the mean time spent in each sequence category	MEDseq_meantime print.MEDseqMeanTime
Show the NEWS file	MEDseq_news
MEDseq gating network standard errors	MEDseq_stderr
MVAD: Transition from school to work	mvad
Plot MEDseq results	plot.MEDseq
Predictions from MEDseq gating networks	fitted.MEDgating predict.MEDgating residuals.MEDgating
Weighted K-Modes Clustering with Tie-Breaking	wKModes

Package: MEDseq 1.4.2

MEDseq: Mixtures of Exponential-Distance Models with Covariates

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