Utilities that estimate, predict and cross-validate the
spatio-temporal model developed for MESA Air.

Johan Lindstrom, Adam Szpiro, Paul D. Sampson, Silas Bergen,
Assaf P.  Oron
'block_matrices.R' 'c_cov_matrices.R' 'c_F_mult.R'
'c_lin_alg.R' 'CV_aux.R' 'CV_estimate.R' 'CV_predict.R'
'deprecated.R' 'internal.R' 'loglikeST.R' 'loglikeST_aux.R'
'loglikeST_derivatives.R' 'MCMC.R' 'SpatioTemporal-data.R'
'SpatioTemporal-package.R' 'STdata.R' 'STdata_processing.R'
'STmodel.R' 'STmodel_combine.R' 'STmodel_estimate.R'
'STmodel_predict.R' 'STmodel_processing.R' 'STmodel_setup.R'
'STmodel_simulate.R' 'temporal_SVD_incomplete_data.R' 'utils.R'

Johan Lindstrom, Adam Szpiro, Paul D. Sampson, Silas Bergen, Assaf P.
Oron

Francesco Pantalone, Roberto Benedetti, Federica Piersimoni
Provides functions to draw spatially balanced samples. It contains fast implementations (C++ via 'Rcpp') of the included sampling methods. In particular, the algorithms to draw spatially balanced samples are pwd() and swd(). These methods use a probability distribution, proportional to the within sample distance. See Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194> for details. Moreover, there is a function hpwd(), which is a heuristic method to achieve approximated samples obtained by pwd() in a faster way. See Benedetti R and Piersimoni F (2017) <arXiv:1710.09116> for details. Finally, there are two functions, stprod() and stsum(), useful to standardize distance matrices in order to achieve fixed sample size using, respectively, the functions pwd() and swd().

c(
person("Francesco", "Pantalone", email = "pantalone.fra@gmail.com", role = c("aut","cre")),
person("Roberto", "Benedetti", role = "aut"),
person("Federica", "Piersimoni", role = "aut")
)
Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194> and Benedetti R and Piersimoni F (2017) <arXiv:1710.09116>. The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'.
Francesco Pantalone [aut, cre],
Roberto Benedetti [aut],
Federica Piersimoni [aut]