We present a statistical method for imputing missing values in accelerometer data. The methodology includes both parametric and semi-parametric multiple imputation under the zero-inflated Poisson lognormal model. It also offers several functions to preprocess accelerometer data before imputation. These include detecting wear and non-wear time, selecting valid days and subjects, and generating plots.
| Version: | 2.2 |
| Depends: | R (≥ 3.5.0), mice, pscl |
| Published: | 2025-05-30 |
| DOI: | 10.32614/CRAN.package.accelmissing |
| Author: | Jung Ae Lee [aut, cre] |
| Maintainer: | Jung Ae Lee <jungaeleeb at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| In views: | MissingData |
| CRAN checks: | accelmissing results |
| Reference manual: | accelmissing.pdf |
| Package source: | accelmissing_2.2.tar.gz |
| Windows binaries: | r-devel: accelmissing_1.4.zip, r-release: accelmissing_2.2.zip, r-oldrel: accelmissing_2.2.zip |
| macOS binaries: | r-release (arm64): accelmissing_2.2.tgz, r-oldrel (arm64): accelmissing_2.2.tgz, r-release (x86_64): accelmissing_2.2.tgz, r-oldrel (x86_64): accelmissing_2.2.tgz |
| Old sources: | accelmissing archive |
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