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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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