Performs 1, 2 and 3D real and complex-valued wavelet transforms,
	nondecimated transforms, wavelet packet transforms, nondecimated
	wavelet packet transforms, multiple wavelet transforms,
	complex-valued wavelet transforms, wavelet shrinkage for
	various kinds of data, locally stationary wavelet time series,
	nonstationary multiscale transfer function modeling, density
	estimation.

Provides scalogram based wavelet tools for time series analysis: wavelet power spectrum, scalogram, windowed scalogram, windowed scalogram difference (see Bolos et al. (2017) <doi:10.1016/j.amc.2017.05.046>), scale index and windowed scale index (Benitez et al. (2010) <doi:10.1016/j.camwa.2010.05.010>).

Computation of approximate potentials for both gradient and non
    gradient fields. It is known from physics that only gradient fields, also
    known as conservative, have a well defined potential function. Here we
    present an algorithm, based on the classical Helmholtz decomposition, to
    obtain an approximate potential function for non gradient fields.
    More information in
    Rodríguez-Sánchez (2020) <doi:10.1371/journal.pcbi.1007788>.

Converts pathways from 'WikiPathways' GPML format or
  'KEGG' KGML format into 'igraph' objects. Includes tools to find all
  cycles in the resulting graphs and determine which ones involve
  negative feedback (inhibition).

Convert, validate, format and elegantly print geographic coordinates and waypoints
   (paired latitude and longitude values) in decimal degrees, degrees and minutes, and degrees,
   minutes and seconds using high performance C++ code to enable rapid conversion and formatting
   of large coordinate and waypoint datasets.

Assessing predictive models of spatial data can be
    challenging, both because these models are typically built for
    extrapolating outside the original region represented by training data
    and due to potential spatially structured errors, with "hot spots" of
    higher than expected error clustered geographically due to spatial
    structure in the underlying data. Methods are provided for assessing
    models fit to spatial data, including approaches for measuring the
    spatial structure of model errors, assessing model predictions at
    multiple spatial scales, and evaluating where predictions can be made
    safely. Methods are particularly useful for models fit using the
    'tidymodels' framework. Methods include Moran's I ('Moran' (1950)
    <doi:10.2307/2332142>), Geary's C ('Geary' (1954)
    <doi:10.2307/2986645>), Getis-Ord's G ('Ord' and 'Getis' (1995)
    <doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement coefficients from
    'Ji' and Gallo (2006) (<doi: 10.14358/PERS.72.7.823>), agreement
    metrics from 'Willmott' (1981) (<doi: 10.1080/02723646.1981.10642213>)
    and 'Willmott' 'et' 'al'. (2012) (<doi: 10.1002/joc.2419>), an
    implementation of the area of applicability methodology from 'Meyer'
    and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an
    implementation of multi-scale assessment as described in 'Riemann'
    'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).