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) <>.

Version: 2.0.0
Depends: R (≥ 3.5.0)
Imports: Amelia, cluster, dfidx, doRNG, doSNOW, dplyr, foreach, graphics, mlr, nnet, parallel, plyr, ranger, rms, stats, stringr, TraMineR, TraMineRextras, utils, mice
Suggests: R.rsp, rmarkdown, testthat (≥ 3.0.0)
Published: 2024-03-27
DOI: 10.32614/CRAN.package.seqimpute
Author: Kevin Emery [aut, cre], Anthony Guinchard [aut], Andre Berchtold [aut], Kamyar Taher [aut]
Maintainer: Kevin Emery <kevin.emery at>
License: GPL-2
NeedsCompilation: no
Materials: NEWS
CRAN checks: seqimpute results


Reference manual: seqimpute.pdf
Vignettes: seqimpute vignette


Package source: seqimpute_2.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): seqimpute_2.0.0.tgz, r-oldrel (arm64): seqimpute_2.0.0.tgz, r-release (x86_64): seqimpute_2.0.0.tgz, r-oldrel (x86_64): seqimpute_2.0.0.tgz
Old sources: seqimpute archive


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