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 spatiotemporal (ST) point patterns
  and/or interactions through the integration of microsimulation 
  (Holm, E., (2017)<doi:10.1002/9781118786352.wbieg0320>) 
  and agent-based models 
  (Bonabeau, E., (2002)<doi:10.1073/pnas.082080899>). 
  The tool enables users to configure the actions of a group of 
  'walkers', which can be agents, objects, individuals, and more. 
  Their engagements with both spatial landscapes (Quaglietta, L. and Porto, M., 
  (2019)<doi:10.1186/s40462-019-0154-8>) 
  and time domains result in specific spatiotemporal point patterns 
  and/or interactions. These emerging spatiotemporal patterns can be 
  visualized, analyzed, and then employed for both spatial and 
  temporal model assessments. Given the growing scarcity of detailed 
  spatiotemporal data, this package offers an alternative dataset 
  for a broad spectrum of studies in both the social and life sciences.