Package: seqimpute 2.1.0

seqimpute: Imputation of Missing Data in Sequence Analysis

Multiple imputation of missing data present in a dataset through the prediction based on either a random forest or a multinomial regression model. Covariates and time-dependent covariates can be included in the model. The prediction of the missing values is based on the method of Halpin (2012) <https://researchrepository.ul.ie/articles/report/Multiple_imputation_for_life-course_sequence_data/19839736>.

Authors:Kevin Emery [aut, cre], Anthony Guinchard [aut], Andre Berchtold [aut], Kamyar Taher [aut]

# Install 'seqimpute' in R:

install.packages('seqimpute', repos = c('https://emerykevin.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

On CRAN:

Last updated 7 days agofrom:53786789bd. Checks:OK: 5. Indexed: yes.

Last update: 2024-03-27
Started: 2024-03-27

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