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.

Methods for decomposing seasonal data: STR (a Seasonal-Trend 
  time series decomposition procedure based on Regression) and Robust STR. In 
  some ways, STR is similar to Ridge Regression and Robust STR can be related to 
  LASSO. They allow for multiple seasonal components, multiple linear covariates 
  with constant, flexible and seasonal influence. Seasonal patterns (for both 
  seasonal components and seasonal covariates) can be fractional and flexible 
  over time; moreover they can be either strictly periodic or have a more 
  complex topology. The methods provide confidence intervals for the estimated 
  components. The methods can also be used for forecasting.