Imports: coda, graphics, sp, stats, optimr

Imports: coda, graphics, sp, stats, optimx

Title: Analysis and Modeling for Geostatistical Count Data
Description: This package provides a variety of functions to analyze and model geostatistical count data with generalized linear spatial models, including
2) posterior sampling with robust MCMC algorithms (in serial or parallel way);
3) perform prediction for unsampled locations;
4) conduct Bayesian model checking procedure to evaluate the goodness of fitting;
5) conduct transformed residual checking procedure.
In the package, seamlessly embedded C++ programs and parallel computing techniques are implemented to speed up the computing processes.
Suggests: coda, distrEx, reldist, snow, snowfall

Suggests: coda, distr, distrEx, reldist, rlecuyer, snowfall

Title: Kriging Method for Spatial Functional Data
Description: A Kriging method for  functional datasets with spatial dependency.
This functional Kriging method avoids the need to estimate the
trace-variogram, and the curve is estimated by minimizing a quadratic
form. The curves in the functional dataset are smoothed using Fourier
series. The functional Kriging of this package is a modification of the

Description: A Kriging method for functional datasets with spatial dependency.

Title: Ordinary Functional Kriging Using Fourier Smoothing and Gaussian
Description: Implementation of the ordinary functional kriging method
implements an alternative method to estimate the trace-variogram using
Fourier Smoothing and Gaussian Quadrature.
Imports: Rcpp, stats, magrittr, orthopolynom

Title: Select Sparse Geoadditive Models for Spatial Prediction
Authors@R: c( person( "Madlene", "Nussbaum", role = c( "cre", "aut" ),
person( "Andreas", "Papritz", role = c( "ths" ),
email =  "andreas.papritz@env.ethz.ch" ) )
Description: A model building procedure to select a sparse geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects and smoothing splines. The resulting covariate set after gradient boosting is further reduced through cross validated backward selection and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study is provided.
Author: Madlene Nussbaum [cre, aut], Andreas Papritz [ths]