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seqimpute: Imputation of Missing Data in Sequence Analysis
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.
Getting started
Browse package contents
Package details |
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| Author | Kevin Emery [aut, cre], Anthony Guinchard [aut], Andre Berchtold [aut], Kamyar Taher [aut] |
| Maintainer | Kevin Emery <kevin.emery@unige.ch> |
| License | GPL-2 |
| Version | 2.2.0 |
| URL | https://github.com/emerykevin/seqimpute |
| Package repository | View on CRAN |
| Installation |
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