Imputation of Missing Data in Sequence Analysis R package seqimpute version 2.2.1 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. Software 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) Kevin Emery <kevin.emery@unige.ch> Comprehensive R Archive Network (CRAN) Kevin Emery [aut, cre], Anthony Guinchard [aut], Andre Berchtold [aut], Kamyar Taher [aut] GPL-2 2026-01-20 application/tgz https://CRAN.R-project.org/package=seqimpute doi:10.32614/CRAN.package.seqimpute