poverty using complex sample survey designs. Wrapper around the
Suggests: testthat, knitr, rmarkdown, IC2, vardpoor, DBI, RODBC,

Suggests: testthat, knitr, rmarkdown, IC2, vardpoor, laeken, DBI,
Packaged: 2020-05-22 16:40:45 UTC; guilherme

Authors@R: c(person("Djalma", "Pessoa", email = "pessoad@gmail.com", role = "aut"),
person("Anthony", "Damico", email= "ajdamico@gmail.com", role= c("aut","cre")),

Suggests: testthat, knitr, rmarkdown, vardpoor, laeken, DBI, RSQLite

Title: Convolution-Based Nonstationary Spatial Modeling
Authors@R: person("Mark D.", "Risser", email = "markdrisser@gmail.com",
Gaussian process models to point-referenced spatial data. The nonstationary
covariance function allows the user to specify the underlying correlation
structure and which spatial dependence parameters should be allowed to
vary over space: the anisotropy, nugget variance, and process variance.
The parameters are estimated via maximum likelihood, using a local
likelihood approach. Also provided are functions to fit stationary spatial
models for comparison, calculate the kriging predictor and standard errors,
and create various plots to visualize nonstationarity.
Imports: stats, graphics, ellipse, fields, geoR, MASS, plotrix,
Packaged: 2015-07-20 22:06:53 UTC; markdrisser
Maintainer: Mark D. Risser <markdrisser@gmail.com>

Packaged: 2015-09-28 14:56:42 UTC; markdrisser

models for comparison, calculate the Kriging predictor and standard errors,

Imports: stats, graphics, ellipse, fields, MASS, plotrix, StatMatch

Title: Probabilistic Species Co-occurrence Analysis in R
Author: Daniel M. Griffith, Joseph A. Veech, and Charles J. Marsh
Maintainer: Daniel M. Griffith <griffith.dan@gmail.com>
Description: This R package applies the probabilistic model of species co-occurrence (Veech 2012) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.