seqimpute: Imputation of Missing Data in Sequence Analysis

Multiple imputation of missing data in a dataset using MICT or MICT-timing methods. The core idea of the algorithms is to fill gaps of missing data, which is the typical form of missing data in a longitudinal setting, recursively from their edges. Prediction is based on either a multinomial or random forest regression model. Covariates and time-dependent covariates can be included in the model.

Version:	2.2.0
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, parallelly
Suggests:	R.rsp, rmarkdown, testthat (≥ 3.0.0)
Published:	2025-01-15
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 unige.ch>
BugReports:	https://github.com/emerykevin/seqimpute/issues
License:	GPL-2
URL:	https://github.com/emerykevin/seqimpute
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	seqimpute results

Documentation:

Reference manual:	seqimpute.pdf
Vignettes:	seqimpute vignette (source)

Downloads:

Package source:	seqimpute_2.2.0.tar.gz
Windows binaries:	r-devel: seqimpute_2.2.0.zip, r-release: seqimpute_2.2.0.zip, r-oldrel: seqimpute_2.2.0.zip
macOS binaries:	r-release (arm64): seqimpute_2.2.0.tgz, r-oldrel (arm64): seqimpute_2.2.0.tgz, r-release (x86_64): seqimpute_2.2.0.tgz, r-oldrel (x86_64): seqimpute_2.2.0.tgz
Old sources:	seqimpute archive

Linking:

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