pMEM implements Predictive Moran's Eigenvector Maps, a method for spatially-explicit prediction of environmental variables using eigen-decomposition of distance-based spatial weighting matrices.

Guénard, G., & Legendre, P. (2024). Spatially-explicit predictions using spatial eigenvector maps. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.14413

pMEM extends classical Moran's Eigenvector Maps (MEM) by:

• Enabling prediction at unsampled locations via predict() methods
• Supporting tunable distance weighting functions (exponential, Gaussian, power, linear, spherical, hyperbolic, hole_effect)
• Optimizing spatial scale parameters via cross-validated model selection
• ntegrating with regression frameworks: linear models, GLMs, elastic net
• Providing fast C++ backend via Rcpp for efficient eigenvector selection
• Supporting asymmetric (directional) spatial processes via complex-valued distance metrics

Ideal for ecologists, geographers, and spatial analysts modeling spatially-autocorrelated variables such as:

• Environmental gradients (depth, temperature, substrate)
• Species distributions and abundances
• Landscape connectivity metrics
• Directional processes (river flow, wind patterns, ocean currents)

install.packages("pMEM")