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Should the need to model the relationship between bivariate data and a response variable arise, two-dimensional (2D) Gaussian models are often the most appropriate choice. For example, Priebe et al. (2003) characterized motion-sensitive neurons in the brains of macaques by fitting 2D-Gaussian functions to neurons’ response rates as spatial and temporal frequencies of visual stimuli were varied. The width and orientation of these fitted 2D-Gaussian surfaces provides insight on whether a neuron is “tuned” to particular spatial or temporal domains. Twodimensional Gaussians are also used in other scientific disciplines such as physics (Kravtsov & Berczynski, 2004; Z. Wu & Guo, 1998), materials sciences (Riekel et al., 1999), and image processing (Hanumantharaju et al., 2013; Ketenci & Gencturk, 2013), particularly in medical imaging (Qadir et al., 2021; J. Wu et al., 2019).

gaussplotR: Fit, Predict and Plot 2D-Gaussians in R