Mixtures of Exponential-Distance Models with Covariates

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Documentation for package ‘MEDseq’ version 1.4.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

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