Identifies implausible anthropometric (e.g., height,
    weight) measurements in irregularly spaced longitudinal datasets, such as those from electronic health records.

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.

Fits the logistic equation to
    microbial growth curve data (e.g., repeated absorbance measurements
    taken from a plate reader over time). From this fit, a variety of
    metrics are provided, including the maximum growth rate,
    the doubling time, the carrying capacity, the area under the logistic
    curve, and the time to the inflection point. Method described in 
    Sprouffske and Wagner (2016) <doi:10.1186/s12859-016-1016-7>.

Decomposes observed growth in agricultural and livestock systems into interpretable component effects. Depending on the application, the total change in output can be attributed to components such as area effect, yield effect, herd or slaughter effect, productivity effect, and interaction effect. Details can be found in Rakshit and Bardhan (2026) <doi:10.1007/s11250-026-04988-w>.