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accelmissing  

Missing Value Imputation for Accelerometer Data
View on CRAN: Click here


Download and install accelmissing package within the R console
Install from CRAN:
install.packages("accelmissing")

Install from Github:
library("remotes")
install_github("cran/accelmissing")

Install by package version:
library("remotes")
install_version("accelmissing", "1.4")



Attach the package and use:
library("accelmissing")
Maintained by
Jung Ae Lee
[Scholar Profile | Author Map]
First Published: 2016-03-25
Latest Update: 2018-04-06
Description:
Imputation for the missing count values in accelerometer data. The methodology includes both parametric and semi-parametric multiple imputations under the zero-inflated Poisson lognormal model. This package also provides multiple functions to pre-process the accelerometer data previous to the missing data imputation. These includes detecting wearing and non-wearing time, selecting valid days and subjects, and creating plots.
How to cite:
Jung Ae Lee (2016). accelmissing: Missing Value Imputation for Accelerometer Data. R package version 1.4, https://cran.r-project.org/web/packages/accelmissing. Accessed 23 Feb. 2025.
Previous versions and publish date:
1.0 (2016-03-25 20:00), 1.1 (2016-07-06 01:11), 1.2 (2018-03-26 19:30)
Other packages that cited accelmissing R package
View accelmissing citation profile
Other R packages that accelmissing depends, imports, suggests or enhances
Complete documentation for accelmissing
Functions, R codes and Examples using the accelmissing R package
Some associated functions: accel.impute . accel.plot.7days . acceldata . acceldata2 . accelimp . accelmissing-package . create.flag . mice.impute.2l.zip.pmm . mice.impute.2l.zipln . mice.impute.2l.zipln.pmm . missing.rate . valid.days . valid.subjects . wear.time.plot . 
Some associated R codes: ACCELMISSING.R .  Full accelmissing package functions and examples
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