Organize a so-called ragged array as generalized arrays, which
	is simply an array with sub-dimensions denoting the subdivision of
	dimensions (grouping of members within dimensions).
	By the margins (names of dimensions and sub-dimensions) in generalized
	arrays, operators and utility functions provided in this package
	automatically match the margins,
	doing map-reduce style parallel computation
	along margins.  Generalized arrays are also cooperative to R's native
	functions that work on simple arrays.

The gasanalyzer R package offers methods for importing, preprocessing,
    and analyzing data related to photosynthetic characteristics (gas exchange,
    chlorophyll fluorescence and isotope ratios). It translates variable names
    into a standard format, and can recalculate derived, physiological 
    quantities using imported or predefined equations. The package also allows
    users to assess the sensitivity of their results to different assumptions
    used in the calculations. 
    See also Tholen (2024) <doi:10.1093/aobpla/plae035>.

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.