seqimpute: Imputation of Missing Data in Sequence Analysis
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: |
2.1.0 |
| 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: |
2024-11-13 |
| 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 |
Documentation:
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