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.

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>.

A compilation of nonlinear growth models.