The developed package can be used to generate a spatial population for different
             levels of relationships among the dependent and auxiliary variables along with spatially
             varying model parameters. A spatial layout is designed as a [0,k-1]x[0,k-1] square region on which observations
             are collected at (k x k) lattice points with a unit distance between any two neighbouring points along the horizontal
             and vertical axes. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).<doi:10.1080/10485252.2018.1499907>.
             The generated spatial population can be utilized in Geographically Weighted Regression model based analysis for studying the 
             spatially varying relationships among the variables. Furthermore, various statistical analysis can be performed on this spatially 
             generated data.

Sparse arrays interpreted as multivariate polynomials.
    Uses 'disordR' discipline (Hankin, 2022,
    <doi:10.48550/ARXIV.2210.03856>).  To cite the package in
    publications please use Hankin (2022) <doi:10.48550/ARXIV.2210.10848>.

The notion of spreading activation is a prevalent metaphor in the cognitive sciences. This package provides the tools for cognitive scientists and psychologists to conduct computer simulations that implement spreading activation in a network representation. The algorithmic method implemented in 'spreadr' subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (2011, Frontiers), who viewed activation as a fixed cognitive resource that could spread among nodes that were connected to each other via edges or connections (i.e., a network). See Vitevitch, M. S., Ercal, G., & Adagarla, B. (2011). Simulating retrieval from a highly clustered network: Implications for spoken word recognition. Frontiers in Psychology, 2, 369. <doi:10.3389/fpsyg.2011.00369> and Siew, C. S. Q. (2019). spreadr: A R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910-929. <doi: 10.3758/s13428-018-1186-5>.