Simulate, estimate and forecast using univariate and multivariate GAS models 
  as described in Ardia et al. (2019) <doi:10.18637/jss.v088.i06>.

Provides functions to import data from several instruments
    commonly used by plant physiologists to measure characteristics
    related to photosynthesis. It provides a standardized list of variable
    names, and several sets of equations to calculate additional variables
    based on the measurements.  These equations have been described by von
    Caemmerer and Farquhar (1981) <doi:10.1007/BF00384257>, Busch et al.
    (2020) <doi:10.1038/s41477-020-0606-6> and Márquez et al. (2021)
    <doi:10.1038/s41477-021-00861-w>. In addition, this package
    facilitates performing sensitivity analyses on variables or
    assumptions used in the calculations.

Provides a genetic algorithm for finding variable
    subsets in high dimensional data with high prediction performance. The
    genetic algorithm can use ordinary least squares (OLS) regression models or
    partial least squares (PLS) regression models to evaluate the prediction
    power of variable subsets. By supporting different cross-validation
    schemes, the user can fine-tune the tradeoff between speed and quality of
    the solution.

Functions for greenhouse gas flux calculation from chamber
    measurements.

Estimation, forecasting, and simulation of generalized
    autoregressive score (GAS) models of Creal, Koopman, and Lucas (2013)
    <doi:10.1002/jae.1279> and Harvey (2013) <doi:10.1017/cbo9781139540933>.
    Model specification allows for various data types and distributions,
    different parametrizations, exogenous variables, joint and separate modeling
    of exogenous variables and dynamics, higher score and autoregressive orders,
    custom and unconditional initial values of time-varying parameters, fixed
    and bounded values of coefficients, and missing values. Model estimation is
    performed by the maximum likelihood method.

Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>.  Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.