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.

Provides functions for creating, displaying, and evaluating stopping rules for safety monitoring in clinical studies. Implements stopping rule methods described in Goldman (1987) <doi:10.1016/0197-2456(87)90153-X>; Geller et al. (2003, ISBN:9781135524388); Ivanova, Qaqish, and Schell (2005) <doi:10.1111/j.1541-0420.2005.00311.x>; Chen and Chaloner (2006) <doi:10.1002/sim.2429>; and Kulldorff et al. (2011) <doi:10.1080/07474946.2011.539924>.

A collection of methods that fit nonlinear distance transformations in multidimensional scaling (MDS) and trade-off the fit with structure considerations to find optimal parameters also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different MDS models in a STOPS framework like Torgerson (classical) scaling, scaling by majorizing a complex function (SMACOF), Sammon mapping, elastic scaling, symmetric SMACOF, spherical SMACOF, s-stress, r-stress, power MDS, power elastic scaling, power Sammon mapping, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS and Isomap. All of these models can also be fit individually with given hyperparameters or by optimizing over hyperparameters based on fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices.