Title: Spatial Data Processing for Decision Making
Description: Methods for processing spatial data for decision-making.
This package is an R implementation of methods provided by the open source software GeoFIS <https://www.geofis.org> (Leroux et al. 2018) <doi:10.3390/agriculture8060073>.
The main functionalities are the management zone delineation (Pedroso et al. 2010) <doi:10.1016/j.compag.2009.10.007> and data aggregation (Mora-Herrera et al. 2020) <doi:10.1016/j.compag.2020.105624>.
Depends: R (>= 4.0.0), sp, data.tree, FisPro (>= 1.1.0)
Imports: methods, Rdpack, foreach, R6, Rcpp (>= 1.0.0), rgeos, nnls
SystemRequirements: GNU make, C++14, gmp, mpfr
Suggests: testthat, rlang, knitr, rmarkdown, RColorBrewer, rgdal
Packaged: 2021-07-21 09:02:54 UTC; jean-luc.lablee

Suggests: testthat, rlang, knitr, rmarkdown, RColorBrewer, rgdal, R.rsp
Packaged: 2021-11-30 07:34:29 UTC; jean-luc.lablee

Imports: methods, utils, stats, Rdpack, foreach, R6, Rcpp (>= 1.0.0),

SystemRequirements: GNU make, C++17, gmp, mpfr

Packaged: 2021-03-05 09:46:11 UTC; jean-luc.lablee

Title: Quantification of the effect of geographic versus environmental
Description: Quantification of the effect of geographic versus environmental isolation on genetic differentiation

X-CRAN-Comment: Orphaned and repaired on 2014-07-21 as Fortran errors

Title: Analysis of light based geolocator data
Author: Simeon Lisovski, Silke Bauer, Tamara Emmenegger
Description: The package provides basic functions for global
positioning based on light intensity measurements over time.
Positioning process includes the determination of sun events, a
discrimination of residency and movement periods, the
calibration of period-specific data and, finally, the

Title: Analysis of Light Based Geolocator Data
Author: Simeon Lisovski, Simon Wotherspoon, Michael Sumner, Silke Bauer, Tamara Emmenegger
Description: Provides basic functions for global

Title: Procedures for Gaussian and Non Gaussian Geostatistical (Large)
Authors@R: c(person("Moreno", "Bevilacqua", role = c("aut", "cre"),
email = "moreno.bevilacqua89@gmail.com",comment = c(ORCID = "0000-0001-8384-840X")),
person("Víctor", "Morales-Oñate", role = "aut",email = "victor.morales@uv.cl",comment = c(ORCID = "0000-0003-1922-6571")),person("Christian", "Caamaño-Carrillo", role = "aut",email = "chcaaman@ubiobio.cl",comment = c(ORCID = "0000-0001-7241-3099")))
Description: Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) simulation and inference  for random fields using standard likelihood and a likelihood approximation  method called  weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial  and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Bevilacqua et al. (2022) <doi:10.1007/s11749-021-00797-5>, Blasi et al. (2022) <doi:10.1016/j.spasta.2022.100596>, Morales-Navarrete et al. (2022) <arXiv:2105.03734>, and a large class of examples and tutorials.
Depends: R (>= 4.1.0), GPvecchia,fields, mapproj, plot3D, shape
Imports: methods, spam, scatterplot3d, dfoptim, dotCall64,
optimParallel, parallel, plotrix, pracma, pbivnorm, zipfR, sn,
nabor, numDeriv, hypergeo, ucminf, lamW, GpGp,VGAM, data.table
Packaged: 2022-05-24 12:55:45 UTC; morenobevilacqua
Author: Moreno Bevilacqua [aut, cre] (<https://orcid.org/0000-0001-8384-840X>),