Title: Regularized Estimation of Dynamic Linear Regression in the
Presence of Autocorrelated Residuals (DREGAR)
Description: A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.

Title: Differential Risk Hotspots in a Linear Network
Description: Performs the identification of differential risk hotspots given a marked point pattern (Diggle 2013) <doi:10.1201/b15326> lying on a linear network (Baddeley, Rubak and Turner 2015) <doi:10.1201/b19708>. The algorithm makes use of a network-constrained version of kernel density estimation (McSwiggan, Baddeley and Nair 2017) <doi:10.1111/sjos.12255>, and then follows a statistical approach to approximate the probability of ocurrence across space for the type of event specified by the user through the marks of the pattern (Kelsall and Diggle 1995) <doi:10.2307/3318678>. The final goal is to detect microzones of the road network where the type of event indicated by the user is overrepresented, considering the network structure provided.
Imports: spatstat, spdep, raster, maptools, sp, utils, stats

Description: Performs the identification of differential risk hotspots (Briz-Redon et al. 2019) <doi:10.1016/j.aap.2019.105278> along a linear network. Given a marked point pattern lying on the linear network, the method implemented uses a network-constrained version of kernel density estimation (McSwiggan et al. 2017) <doi:10.1111/sjos.12255> to approximate the probability of occurrence across space for the type of event specified by the user through the marks of the pattern (Kelsall and Diggle 1995) <doi:10.2307/3318678>. The goal is to detect microzones of the linear network where the type of event indicated by the user is overrepresented.
Imports: graphics, grDevices, maptools, PBSmapping, raster, sp,

spatstat.geom, spatstat.core, spatstat.linnet, spatstat (>=