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 |