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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) <https://researchrepository.ul.ie/articles/report/Multiple_imputation_for_life-course_sequence_data/19839736>.
| 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 |
| Author: | Kevin Emery [aut, cre], Anthony Guinchard [aut], Andre Berchtold [aut], Kamyar Taher [aut] |
| Maintainer: | Kevin Emery <kevin.emery at unige.ch> |
| 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: seqimpute_2.0.0.zip, r-release: seqimpute_2.0.0.zip, r-oldrel: seqimpute_2.0.0.zip |
| 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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