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-dependant 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>.

Version: 1.8
Depends: R (≥ 3.5.0)
Imports: Amelia, rms, stringr, TraMineR, cluster, swfscMisc, plyr, dplyr, dfidx, mice, foreach, parallel, doRNG, doSNOW, ranger, mlr, nnet
Published: 2022-11-07
Author: Andre Berchtold [aut, cre], Anthony Guinchard [aut], Kevin Emery [aut], Kamyar Taher [aut]
Maintainer: Andre Berchtold <andre.berchtold at unil.ch>
License: GPL-2
NeedsCompilation: no
CRAN checks: seqimpute results

Documentation:

Reference manual: seqimpute.pdf

Downloads:

Package source: seqimpute_1.8.tar.gz
Windows binaries: r-devel: seqimpute_1.8.zip, r-release: seqimpute_1.8.zip, r-oldrel: seqimpute_1.8.zip
macOS binaries: r-release (arm64): seqimpute_1.8.tgz, r-oldrel (arm64): seqimpute_1.8.tgz, r-release (x86_64): seqimpute_1.8.tgz
Old sources: seqimpute archive

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