# Imputation of Missing Data in Sequence Analysis

## Documentation for package ‘seqimpute’ version 2.2.1

- 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