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GrowthCurveME

Mixed-Effects Modeling for Growth Data

v0.1.11 · Apr 11, 2025 · GPL (>= 3)

Description

Simple and user-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.

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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026

Dependency Network

Dependencies Reverse dependencies dplyr flextable ggplot2 investr knitr magrittr minpack.lm moments patchwork (>= 1.2.0) rlang saemix stringr tibble tidyr viridis GrowthCurveME

Version History

new 0.1.11 Mar 10, 2026