Mixed-Effects Modeling for Growth Data
Fit an exponential mixed-effects regression model
Fit a Gompertz mixed-effects regression model
Fit a growth function using mixed-effects regression modeling
Create residual diagnostic plots for growth model
Create a printable table of the summarized growth model result reporti...
Generate growth vs time plots
Fit a linear mixed-effects regression model
Fit a logistic mixed-effects regression model
Summarize least-squares growth model object and data
Summarize mixed-effects growth model object and data
Summarize growth model object and data
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 [email protected] with any questions.
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