This article illustrates the use of the bcmixed package and focuses on the two main functions: bcmarg and bcmmrm. The bcmarg function provides inference results for a marginal model of a mixed ef fect model using the Box–Cox transformation. The bcmmrm function provides model median inferences based on the mixed effect models for repeated measures analysis using the Box–Cox transformation for longitudinal randomized clinical trials. Using the bcmmrm function, analysis results with high power and high interpretability for treatment effects can be obtained for longitudinal randomized clinical trials with skewed outcomes. Further, the bcmixed package provides summarizing and visualization tools, which would be helpful to write clinical trial reports.
bcmixed, nlme, glme, lme4, CLME, PLmixed, MCMCglmm, ggplot2, MissMech
Psychometrics, SocialSciences, Econometrics, Environmetrics, OfficialStatistics, Phylogenetics, SpatioTemporal, Bayesian, ChemPhys, Finance, Spatial, Survival, TeachingStatistics
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Maruo, et al., "bcmixed: A Package for Median Inference on Longitudinal Data with the Box–Cox Transformation", The R Journal, 2021
BibTeX citation
@article{RJ-2021-083,
author = {Maruo, Kazushi and Ishii, Ryota and Yamaguchi, Yusuke and Gosho, Masahiko},
title = {bcmixed: A Package for Median Inference on Longitudinal Data with the Box–Cox Transformation},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-083},
doi = {10.32614/RJ-2021-083},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {253-265}
}