SVDGP : Singular Value Decomposition and Gaussian process modelling

SVDGP package provides tools for the decomposition, modeling, and prediction of spatio-temporal functions represented as:

$$f : (x, t) \in \mathbb{R}^{d} \times \mathbb{R} \mapsto f(x, t) \in \mathbb{R},$$

where the function $f(x, t)$ is allways observed for a spatial locations $x \in \mathbb{R}^{d}$ at discrete time points $t_{1}, \ldots, t_{N_t}$.

Installation

You can install the latest version of the package manually or directly from GitHub.

Option 1: Install from GitHub (Recommended)

Make sure you have the devtools package installed, then use:

install.packages("devtools")
devtools::install_github("TheseAdama/SVDGP")

Option 2: Manual Installation (Download & Install ZIP)

1. Download the ZIP or TAR.GZ file Download the latest version of the package in ZIP or TAR.GZ format.

   • For Windows: SVDGP_x.y.z.zip
   • For Linux/macOS: SVDGP_x.y.z.tar.gz

2. Install the package manually in R

   Open your R session and run one of the following commands, replacing the file path with where you downloaded the archive:

   • On Windows:

     install.packages("path/to/SVDGP_x.y.z.zip", repos = NULL, type = "win.binary")

   • On Linux/macOS:

     install.packages("path/to/SVDGP_x.y.z.tar.gz", repos = NULL, type = "source")

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After installation, load the package:

library(SVDGP)