Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions
  by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The
  method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. The 'spatPomp' package extends 'pomp' to include algorithms taking advantage of the spatial structure in order to assist with handling high dimensional processes. See Asfaw et al. (2024) <doi:10.48550/arXiv.2101.01157> for further description of the package.

Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.

Detects spatial and temporal groups in GPS relocations
    (Robitaille et al. (2019) <doi:10.1111/2041-210X.13215>).  It can be
    used to convert GPS relocations to gambit-of-the-group format to build
    proximity-based social networks, and perform data-stream randomization 
    methods suitable for GPS data. Also provides measures of intragroup social 
    dynamics including distance and direction to leaders, centroids and nearest 
    neighbours.

Contains all the datasets for the 'spatstat' family of packages.

Functionality for exploratory data analysis and nonparametric analysis of
	     spatial data, mainly spatial point patterns,
	     in the 'spatstat' family of packages.
	     (Excludes analysis of spatial data on a linear network,
	     which is covered by the separate package 'spatstat.linnet'.)
	     Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.