The R Journal: article published in 2014, volume 6:2

Farewell's Linear Increments Model for Missing Data: The FLIM package
Rune Hoff, Jon Michael Gran and Daniel Farewell , The R Journal (2014) 6:2, pages 137-150.

Abstract Missing data is common in longitudinal studies. We present a package for Farewell’s Linear Increments Model for Missing Data (the FLIM package), which can be used to fit linear models for observed increments of longitudinal processes and impute missing data. The method is valid for data with regular observation patterns. The end result is a list of fitted models and a hypothetical complete dataset corresponding to the data we might have observed had individuals not been missing. The FLIM package may also be applied to longitudinal studies for causal analysis, by considering counterfactual data as missing data for instance to compare the effect of different treatments when only data from observational studies are available. The aim of this article is to give an introduction to the FLIM package and to demonstrate how the package can be applied.

Received: 2014-05-25; online 2015-01-09
CRAN packages: zoo
CRAN Task Views implied by cited CRAN packages: Econometrics, Environmetrics, Finance, TimeSeries

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2014-030,
  author = {Rune Hoff and Jon Michael Gran and Daniel Farewell},
  title = {{Farewell's Linear Increments Model for Missing Data: The
          FLIM package}},
  year = {2014},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2014-030},
  url = {https://doi.org/10.32614/RJ-2014-030},
  pages = {137--150},
  volume = {6},
  number = {2}
}