Trajectory data formed by human or animal movement is often marked 
    by periods of movement interspersed with periods of standing still. It is
    often of interest to researchers to separate geolocation trajectories of
    latitude/longitude points by clustering consecutive locations to produce a
    model of this behavior. This package implements the Stay Point detection
    algorithm originally described in Ye (2009) <doi:10.1109/MDM.2009.11> that
    uses time and distance thresholds to characterize spatial regions as
    'stops'. This package also implements the concept of merging described in
    Montoliu (2013) <doi:10.1007/s11042-011-0982-z> as stay point region
    estimation, which allows for clustering of temporally adjacent stops for
    which distance between the midpoints is less than the provided threshold.
    GPS-like data from various sources can be used, but the temporal thresholds
    must be considered with respect to the sampling interval, and the spatial 
    thresholds must be considered with respect to the measurement error.

Implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models).

Toolbox for different kinds of spatio-temporal analyses to be performed on observed point patterns, following the growing stream of literature on point process theory. This R package implements functions to perform different kinds of analyses on point processes, proposed in the papers (Siino, Adelfio, and Mateu 2018<doi:10.1007/s00477-018-1579-0>; Siino et al. 2018<doi:10.1002/env.2463>; Adelfio et al. 2020<doi:10.1007/s00477-019-01748-1>; D’Angelo, Adelfio, and Mateu 2021<doi:10.1016/j.spasta.2021.100534>; D’Angelo, Adelfio, and Mateu 2022<doi:10.1007/s00362-022-01338-4>; D’Angelo, Adelfio, and Mateu 2023<doi:10.1016/j.csda.2022.107679>). The main topics include modeling, statistical inference, and simulation issues on spatio-temporal point processes on Euclidean space and linear networks.