Construction and smart selection of Gaussian process models
	for analysis of computer experiments
	with emphasis on treatment of functional inputs that are regularly sampled. This package
	offers: (i) flexible modeling of functional-input regression
	problems through the fairly general Gaussian process model; (ii)
	built-in dimension reduction for functional inputs; (iii)
	heuristic optimization of the structural parameters of the model
	(e.g., active inputs, kernel function, type of distance).
	An in-depth tutorial in the use of funGp is provided in
	Betancourt et al. (2024) <doi:10.18637/jss.v109.i05> and
	Metamodeling background is provided in
	Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>.
	The algorithm for structural parameter optimization is described
	in <https://hal.science/hal-02532713>.

Similar to base's unique function, only optimized for working with 
    data frames, especially those that contain date-time columns.

Provides four addons for analyzing trends and
    unit roots in financial time series: (i) functions for the density
    and probability of the augmented Dickey-Fuller Test, (ii) functions 
    for the density and probability of MacKinnon's unit root test 
    statistics, (iii) reimplementations for the ADF and MacKinnon
    Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's
    unit root test suite.

Compare variables of interest between (potentially large
    numbers of) spatial interactions and meta-variables. Spatial variables
    are summarized using K, or other, functions, and projected for use in
    a modified random forest model. The model allows comparison of
    functional and non-functional variables to each other and to noise,
    giving statistical significance to the results. Included are
    preparation, modeling, and interpreting tools along with example
    datasets, as described in VanderDoes et al., (2023)
    <doi:10.1101/2023.07.18.549619>.

Allows generating heatmap-like visualisations for data 
  frames. Funky heatmaps can be fine-tuned by providing annotations of the 
  columns and rows, which allows assigning multiple palettes or geometries
  or grouping rows and columns together in categories.
  Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>.