Depends: R (>= 2.15.0), geoR, gstat, genalg, limSolve, MASS, plyr,

Depends: R (>= 2.15.0), gstat, genalg, MASS, sp
Imports: nloptr, fields, gsl, plyr, TeachingDemos, sgeostat
Packaged: 2014-10-08 13:55:53 UTC; asantacruzdelgado

Title: Geostatistical Analysis and Design of Optimal Spatial Sampling
Depends: R (>= 2.15.0), gstat, genalg, MASS, sp, minqa
Imports: limSolve, fields, gsl, plyr, TeachingDemos, sgeostat
Description: Estimation of the variogram through trimmed mean, radial basis
functions (optimization, prediction and cross-validation), summary

Imports: limSolve, fields, gsl, plyr, TeachingDemos, sgeostat,
grDevices, stats, methods, graphics, utils

Title: Spatio-Temporal; Inverse Distance Weighting and Radial Basis
Author: Carlos Melo, Oscar Melo <oomelom@unal.edu.co>
Depends: R (>= 2.15.0), FD, StatMatch, fields, sp
Description: Spatio-temporal: Inverse Distance Weighting (IDW) and radial basis functions; optimization, prediction, summary statistics from leave-one-out cross-validation, adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.
Packaged: 2015-06-23 02:49:21 UTC; Carlos Melo

Title: Spatio-Temporal Radial Basis Functions with Distance-Based
Methods (Optimization, Prediction and Cross Validation)
person("Oscar", "Melo", role = "aut"),person("Sandra", "Melo", role = "aut"))
Depends: R (>= 3.5.0), FD, StatMatch, fields, sp
Imports: minqa, limSolve, gsl, geospt, grDevices, methods, stats, utils
Description: Spatio-temporal radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, Adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.
Author: Carlos Melo [aut, cre] (<https://orcid.org/0000-0002-5598-1913>),

person("Oscar", "Melo", role = "aut", comment = c(ORCID = "0000-0002-0296-4511")),person("Sandra", "Melo", role = "aut"))

Description: For Bayesian inference with spatial data, provides exploratory analysis tools, multiple spatial model specifications, spatial model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)). Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.
Imports: spdep (>= 1.1-8), sf, ggplot2 (>= 3.0.0), methods, graphics,
stats, MASS, truncnorm, signs, gridExtra, utils, Matrix (>=
Suggests: testthat, knitr, rmarkdown, bayesplot
Author: Connor Donegan [aut, cre] (<https://orcid.org/0000-0002-9698-5443>),