Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions.

Provides geographical faceting functionality for 'ggplot2'.
    Geographical faceting arranges a sequence of plots of data for different
    geographical entities into a grid that preserves some of the geographical
    orientation.

Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API <https://geo.stat.fi/geoserver/index.html>, the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations.

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
    method proposed by Giraldo (2011) <doi:10.1007/s10651-010-0143-y>.

Implementation of the ordinary functional kriging method 
    proposed by Giraldo (2011) <doi:10.1007/s10651-010-0143-y>. This
    implements an alternative method to estimate the trace-variogram using
    Fourier Smoothing and Gaussian Quadrature.

A model building procedure to build parsimonious 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, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination 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 in Berne (Switzerland) is provided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) <doi:10.5194/soil-3-191-2017>.