Spatial downscaling of coarse grid mapping to fine grid
    mapping using predictive covariates and a model fitted using the 'caret'
    package. The original dissever algorithm was published by Malone et al. 
    (2012) <doi:10.1016/j.cageo.2011.08.021>, and extended by Roudier et al.
    (2017) <doi:10.1016/j.compag.2017.08.021>.

Tools to fit sample selection models in case of discrete response 
  variables, through a parametric formulation which represents a natural 
  extension of the well-known Heckman selection model are provided in the 
  package. The response variable can be of Bernoulli, Poisson or Negative 
  Binomial type. The sample selection mechanism allows to choose among a 
  Normal, Logistic or Gumbel distribution.

A simple way of fitting detection functions to distance sampling
    data for both line and point transects. Adjustment term selection, left and
    right truncation as well as monotonicity constraints and binning are
    supported. Abundance and density estimates can also be calculated (via a
    Horvitz-Thompson-like estimator) if survey area information is provided. See
    Miller et al. (2019) <doi:10.18637/jss.v089.i01> for more information on
    methods and <https://examples.distancesampling.org/> for example analyses.

Provides tools for constructing, manipulating and using distance metrics.

Calculates distances from point locations to features.
    The usual approach for eg. resource selection function analyses is to
    generate a complete distance to features surface then sample it with your 
    observed and random points. Since these raster based approaches can be
    pretty costly with large areas, and often lead to memory issues in R, 
    the distanceto package opts to compute these distances using
    efficient, vector based approaches. As a helper, there's a decidedly 
    low-res raster based approach for visually inspecting your region's 
    distance surface. But the workhorse is distance_to.

Provides tools to assess the dissimilarity between multivariate time-series. It is based on the psi measure described  by Birks and Gordon (1985 <doi:10.1002/jqs.3390020110>), which computes dissimilarity between irregular time-series constrained by sample order. However, in this package the original idea has been extended to work with any kind of multivariate time-series, no matter whether they are regular, irregular, aligned or unaligned. Furthermore, the package allows to assess the significance of dissimilarity values by applying a restricted permutation test, allows to measure the contribution of individual variables to dissimilarity, and offers tools to transfer attributes (generally time or age, but other are possible) between sequences based on the similarity of their samples.