A Nonparametric Doubly Robust Test for Continuous Treatment
Effect
person(given = "Guangwei",family = "Weng",role = c("aut", "cre"), email = "wengx076@umn.edu")
Implement the statistical test proposed in Weng et al. (2021) to test whether
the average treatment effect curve is constant and whether a discrete covariate is a significant effect modifier.

Regularized Estimation of Dynamic Linear Regression in the
Presence of Autocorrelated Residuals (DREGAR)
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.

Differential Risk Hotspots in a Linear Network
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.
spatstat, spdep, raster, maptools, sp, utils, stats

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.
graphics, grDevices, maptools, PBSmapping, raster, sp,
spatstat, spdep, stats, utils