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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.2.1 |
rolling linux/jammy R-4.5 | seqimpute_2.2.1.tar.gz |
1.0 MiB |
2.2.1 |
rolling linux/noble R-4.5 | seqimpute_2.2.1.tar.gz |
1.0 MiB |
2.2.1 |
rolling source/ R- | seqimpute_2.2.1.tar.gz |
908.2 KiB |
2.2.1 |
latest linux/jammy R-4.5 | seqimpute_2.2.1.tar.gz |
1.0 MiB |
2.2.1 |
latest linux/noble R-4.5 | seqimpute_2.2.1.tar.gz |
1.0 MiB |
2.2.1 |
latest source/ R- | seqimpute_2.2.1.tar.gz |
908.2 KiB |
2.2.1 |
2026-04-26 source/ R- | seqimpute_2.2.1.tar.gz |
908.2 KiB |
2.2.1 |
2026-04-23 source/ R- | seqimpute_2.2.1.tar.gz |
908.2 KiB |
2.2.1 |
2026-04-09 windows/windows R-4.5 | seqimpute_2.2.1.zip |
1.0 MiB |
2.2.0 |
2025-04-20 source/ R- | seqimpute_2.2.0.tar.gz |
548.7 KiB |