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GrowthCurveME  

Mixed-Effects Modeling for Growth Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("GrowthCurveME", "0.1.0")



Attach the package and use:
library("GrowthCurveME")
Maintained by
Anand Panigrahy
[Scholar Profile | Author Map]
First Published: 2024-12-17
Latest Update: 2024-12-17
Description:
Simple anduser-friendly wrappers to the 'saemix' package for performing linear and non-linear mixed-effects regression modeling for growth data to account for clustering or longitudinal analysis via repeated measurements. The package allows users to fit a variety of growth models, including linear, exponential, logistic, and 'Gompertz' functions. For non-linear models, starting values are automatically calculated using initial least-squares estimates. The package includes functions for summarizing models, visualizing data and results, calculating doubling time and other key statistics, and generating model diagnostic plots and residual summary statistics. It also provides functions for generating publication-ready summary tables for reports. Additionally, users can fit linear and non-linear least-squares regression models if clustering is not applicable. The mixed-effects modeling methods in this package are based on Comets, Lavenu, and Lavielle (2017) <doi:10.18637/jss.v080.i03> as implemented in the 'saemix' package. Please contact us at models@dfci.harvard.edu with any questions.
How to cite:
Anand Panigrahy (2024). GrowthCurveME: Mixed-Effects Modeling for Growth Data. R package version 0.1.0, https://cran.r-project.org/web/packages/GrowthCurveME. Accessed 17 Feb. 2025.
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