Most of the current methods explore spatial association using 
             observations at sample locations, which are defined as the first 
             dimension of spatial association (FDA). The proposed concept of
             the second dimension of spatial association (SDA), 
             as described in Yongze Song (2022) 
             <doi:10.1016/j.jag.2022.102834>, aims to extract
             in-depth information about the geographical environment from 
             locations outside sample locations for exploring spatial
             association.

These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries  data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying  correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from  fleet segment to metier  level.

Estimating parameters of site clusters on 2D & 3D square lattice with various lattice sizes, relative fractions of open sites (occupation probability), iso- & anisotropy, von Neumann & Moore (1,d)-neighborhoods, described by Moskalev P.V. et al. (2011) <arXiv:1105.2334v1>.

Functions to estimate the density and size of a spatially 
  distributed animal population sampled with an array of passive detectors, 
  such as traps, or by searching polygons or transects. Models incorporating 
  distance-dependent detection are fitted by maximizing the likelihood. 
  Tools are included for data manipulation and model selection.

Tools for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'. Extensions in version 2.5.0 include costing and evaluation of detector spacing.

Allow sharing sensitive information, for example passwords,
    'API' keys, etc., in R packages, using public key cryptography.

Fast and memory-efficient streaming hash functions and base64
    encoding / decoding. Hashes strings and raw vectors directly. Stream hashes
    files potentially larger than memory, as well as in-memory objects through
    R's serialization mechanism. Implementations include the SHA-256, SHA-3 and
    'Keccak' cryptographic hash functions, SHAKE256 extendable-output function
    (XOF), and 'SipHash' pseudo-random function.