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-dependant 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: | 1.8 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Amelia, rms, stringr, TraMineR, cluster, swfscMisc, plyr, dplyr, dfidx, mice, foreach, parallel, doRNG, doSNOW, ranger, mlr, nnet |
| Published: | 2022-11-07 |
| Author: | Andre Berchtold [aut, cre], Anthony Guinchard [aut], Kevin Emery [aut], Kamyar Taher [aut] |
| Maintainer: | Andre Berchtold <andre.berchtold at unil.ch> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | seqimpute results |
| Reference manual: | seqimpute.pdf |
| Package source: | seqimpute_1.8.tar.gz |
| Windows binaries: | r-devel: seqimpute_1.8.zip, r-release: seqimpute_1.8.zip, r-oldrel: seqimpute_1.8.zip |
| macOS binaries: | r-release (arm64): seqimpute_1.8.tgz, r-oldrel (arm64): seqimpute_1.8.tgz, r-release (x86_64): seqimpute_1.8.tgz, r-oldrel (x86_64): seqimpute_1.8.tgz |
| Old sources: | seqimpute archive |
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