Estimates a collection of time-indexed functions under
    either of Gaussian process (GP) or intrinsic Gaussian Markov
    random field (iGMRF) prior formulations where a Dirichlet process
    mixture allows sub-groupings of the functions to share the same
    covariance or precision parameters.  The GP and iGMRF formulations
    both support any number of additive covariance or precision terms,
    respectively, expressing either or both of multiple trend and
    seasonality.

Run grass growth simulations using a grass growth model based 
    on ModVege (Jouven, M., P. Carrère, and R. Baumont "Model Predicting 
    Dynamics of Biomass, Structure and Digestibility of Herbage in Managed 
    Permanent Pastures. 1. Model Description." (2006) 
    <doi:10.1111/j.1365-2494.2006.00515.x>). The implementation in
    this package contains a few additions to the above cited version of ModVege,
    such as simulations of management decisions, and influences of snow cover.
    As such, the model is fit to simulate grass growth in mountainous 
    regions, such as the Swiss Alps. The package also contains routines for 
    calibrating the model and helpful tools for analysing model outputs and 
    performance.

Functions for fitting various normal theory (growth
    curve) and elliptically-contoured repeated measurements models
    with ARMA and random effects dependence.

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.