Nothing
#' varycoef: Modeling Spatially Varying Coefficients
#'
#' This package offers functions to estimate and predict Gaussian process-based
#' spatially varying coefficient (SVC) models. Briefly described, one
#' generalizes a linear regression equation such that the coefficients are no
#' longer constant, but have the possibility to vary spatially. This is enabled
#' by modeling the coefficients using Gaussian processes with (currently) either
#' an exponential or spherical covariance function. The advantages of such SVC
#' models are that they are usually quite easy to interpret, yet they offer a
#' very high level of flexibility.
#'
#'
#' @section Estimation and Prediction:
#' The ensemble of the function \code{\link{SVC_mle}} and the method
#' \code{predict} estimates the defined SVC model and gives predictions of the
#' SVC as well as the response for some pre-defined locations. This concept
#' should be rather familiar as it is the same for the classical regression
#' (\code{\link{lm}}) or local polynomial regression (\code{\link{loess}}),
#' to name a couple. As the name suggests, we are using a \emph{maximum
#' likelihood estimation} (MLE) approach in order to estimate the model. The
#' predictor is obtained by the empirical best linear unbiased predictor.
#' to give location-specific predictions. A detailed tutorial with examples is
#' given in a vignette; call \code{vignette("example", package = "varycoef")}.
#' We also refer to the original article Dambon et al. (2021) which lays the
#' methodological foundation of this package.
#'
#'
#' With the before mentioned \code{\link{SVC_mle}} function one gets an object
#' of class \code{\link{SVC_mle}}. And like the method \code{predict} for
#' predictions, there are several more methods in order to diagnose the model,
#' see \code{methods(class = "SVC_mle")}.
#'
#' @section Variable Selection:
#' As of version 0.3.0 of \code{varycoef}, a joint variable selection of both
#' fixed and random effect of the Gaussian process-based SVC model is
#' implemented. It uses a \emph{penalized maximum likelihood estimation} (PMLE)
#' which is implemented via a gradient descent. The estimation of the shrinkage
#' parameter is available using a \emph{model-based optimization} (MBO). Here,
#' we use the framework by Bischl et al. (2017). The methodological foundation
#' of the PMLE is described in Dambon et al. (2022).
#'
#' @examples
#' vignette("manual", package = "varycoef")
#' methods(class = "SVC_mle")
#'
#' @author Jakob Dambon
#'
#' @references Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J.,
#' Lang, M. (2017). \emph{mlrMBO: A Modular Framework for Model-Based
#' Optimization of Expensive Black-Box Functions},
#' ArXiv preprint \url{https://arxiv.org/abs/1703.03373}
#'
#' Dambon, J. A., Sigrist, F., Furrer, R. (2021).
#' \emph{Maximum likelihood estimation of spatially varying coefficient
#' models for large data with an application to real estate price prediction},
#' Spatial Statistics 41 100470 \doi{10.1016/j.spasta.2020.100470}
#'
#' Dambon, J. A., Sigrist, F., Furrer, R. (2022).
#' \emph{Joint Variable Selection of both Fixed and Random Effects for
#' Gaussian Process-based Spatially Varying Coefficient Models},
#' International Journal of Geographical Information Science
#' \doi{10.1080/13658816.2022.2097684}
#'
#' @docType package
#' @name varycoef
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.