seqimpute
2.2.1Imputation of Missing Data in Sequence Analysis
Overview
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.
Install
Health
- OK2026-08-0413 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-05-0213 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-2511 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 90%
- Documented parameters
- 100%
- Return-value docs
- 82%
- References docs
- 7%
Downloads
Repository
Stars over time
Repository practices
4 development-tooling and community-health practices detected across 3 families in the upstream repository
Checks run against github.com/emerykevin/seqimpute on 2026-08-16.
Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
6 releases. Pick two to compare their code metrics. R releases are shown for context.
Package metadata
- First published
- 2022-09-08
- Total releases
- 6 / 4 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 3.3 KB / 1 file
- Download size
- 906 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
Cite
Cite this package
Run in R for the authors' preferred citation:
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