Utilities to estimate parameters of the models with survival functions 
             induced by stochastic covariates. Miscellaneous functions for data preparation 
             and simulation are also provided. For more information, see: 
             (i)"Stochastic model for analysis of longitudinal data on aging and mortality" 
             by Yashin A. et al. (2007), 
             Mathematical Biosciences, 208(2), 538-551, <DOI:10.1016/j.mbs.2006.11.006>;
             (ii) "Health decline, aging and mortality: how are they related?" 
             by Yashin A. et al. (2007), 
             Biogerontology 8(3), 291(302), <DOI:10.1007/s10522-006-9073-3>.

Many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena are covered in 'stpp'. This package provides statistical tools for analyzing the global and local second-order properties of spatio-temporal point processes, including estimators of the space-time inhomogeneous K-function and pair correlation function. It also includes tools to get static and dynamic display of spatio-temporal point patterns. See Gabriel et al (2013) <doi:10.18637/jss.v053.i02>.

Estimation of model parameters for marked Hawkes process.
    Accounts for missing data in the estimation of the parameters.
    Technical details found in (Tucker et al., 2019 <DOI:10.1016/j.spasta.2018.12.004>).

Generates artificial point patterns marked by their
  spatial and temporal signatures. The resulting point cloud
  may exhibit inherent interactions between both signatures.
  The simulation integrates microsimulation 
  (Holm, E., (2017)<doi:10.1002/9781118786352.wbieg0320>) 
  and agent-based models (Bonabeau, E., (2002)<doi:10.1073/pnas.082080899>),
  beginning with the configuration of movement characteristics for 
  the specified agents (referred to as 'walkers') and their interactions 
  within the simulation environment. These interactions 
  (Quaglietta, L. and Porto, M., (2019)<doi:10.1186/s40462-019-0154-8>) 
  result in specific spatiotemporal patterns that can be visualized, 
  analyzed, and used for various analytical purposes. 
  Given the growing scarcity of detailed spatiotemporal data 
  across many domains, this package provides an alternative data source 
  for applications in social and life sciences.