Description: N methods are used to measure each of n items. This data
is used to estimate the accuracy and precision of the methods.
Maximum likelihood estimation is used for the precision

Title: Bayesian Regression with Meshed Gaussian Processes
Description: Fits Bayesian spatial or spatiotemporal multivariate regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889> and Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>.
Imports: Rcpp (>= 1.0.5), stats, dplyr, glue, rlang, magrittr
Suggests: ggplot2, abind, rmarkdown, knitr, tidyr

Description: Fits Bayesian spatial or spatiotemporal multivariate regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889> and Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>. Funded by ERC grant 856506 and NIH grant R01ES028804.

Description: Fits Bayesian spatial or spatiotemporal multivariate regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889>, Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>, Peruzzi and Dunson (2022) <arXiv:2201.10080>. Funded by ERC grant 856506 and NIH grant R01ES028804.

Description: Fits Bayesian regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889>, Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>, Peruzzi and Dunson (2022) <arXiv:2201.10080>. Funded by ERC grant 856506 and NIH grant R01ES028804.
Imports: Rcpp (>= 1.0.5), stats, dplyr, glue, rlang, magrittr, FNN

Title: Simplification of Surface Triangular Meshes with Associated
Authors@R: c(person("Franco", "Dassi", email = "franco.dassi@unimib.it", role = "aut"),
person("Bree", "Ettinger", email = "", role = "aut"),
person("Marco", "Martinolli", email = "marco.martinolli@mail.polimi.it", role = "aut"),
person("Simona", "Perotto", email = "simona.perotto@polimi.it", role = "aut"),
person("Laura M.", "Sangalli", email = "laura.sangalli@polimi.it", role = "aut"),
person("Stefano", "Ubbiali", email = "stefano.ubbiali@mail.polimi.it", role = c("aut","cre")))
Description: Iterative simplification strategy for surface triangular meshes (2.5D meshes) with associated data. Each iteration corresponds to an edge collapse where the selection of the edge to contract is driven by a cost functional that depends both on the geometry of the mesh than on the distribution of the data locations over the mesh. The library can handle both zero and higher genus surfaces. The package has been designed to be fully compatible with the R package 'fdaPDE', which implements regression models with partial differential regularizations, making use of the Finite Element Method. In the future, the functionalities provided by the current package may be directly integrated into 'fdaPDE'.