Imputation of Missing Data in Sequence Analysis

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Documentation for package ‘seqimpute’ version 2.1.0

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

Help Pages

addcluster	Function that adds the clustering result to a 'seqimp' object obtained with the 'seqimpute' function
fromseqimp	Transform an object of class 'seqimp' into a dataframe or a 'mids' object
gameadd	Example data set: Game addiction
plot.seqimp	Plot a 'seqimp' object
print.seqimp	Print a 'seqimp' object
seqaddNA	Generation of missing on longitudinal categorical data.
seqcomplete	Extract all the trajectories without missing value.
seqimpute	seqimpute: Imputation of missing data in longitudinal categorical data
seqmissfplot	Plot the most common patterns of missing data.
seqmissimplic	Identification and visualization of states that best characterize sequences with missing data
seqmissIplot	Plot all the patterns of missing data.
seqQuickLook	Summary of the types of gaps among a dataset
seqTrans	Spotting impossible transitions in longitudinal categorical data
seqwithmiss	Extract all the trajectories with at least one missing value
summary.seqimp	Summary of a 'seqimp' object