seqimpute
Imputation of Missing Data in Sequence Analysis
v2.2.1
·
Jan 20, 2026
·
GPL-2
Description
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.
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| r-release-windows-x86_64 | OK |