Computation of one-, two- and three-dimensional 
  pseudo-observations based on recurrent events and terminal events. 
  Generalised linear models are fitted using generalised estimating 
  equations. Technical details on the bivariate procedure can be found 
  in "Bivariate pseudo-observations for recurrent event analysis with 
  terminal events" (Furberg et al., 2021) <doi:10.1007/s10985-021-09533-5>.

Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data.

Includes algorithms to facilitate the assessment of extinction risk of species according to the IUCN (International Union for Conservation of Nature, see <https://iucn.org/> for more information) red list criteria.

Contains implementations of recurrent event data analysis routines
    including (1) survival and recurrent event data simulation from
    stochastic process point of view by the thinning method
    proposed by Lewis and Shedler (1979) <doi:10.1002/nav.3800260304>
    and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976),
    (2) the mean cumulative function (MCF) estimation by the
    Nelson-Aalen estimator of the cumulative hazard rate function,
    (3) two-sample recurrent event responses comparison with the pseudo-score
    tests proposed by Lawless and Nadeau (1995) <doi:10.2307/1269617>,
    (4) gamma frailty model with spline rate function following
    Fu, et al. (2016) <doi:10.1080/10543406.2014.992524>.

The aim of this package is to manipulate relational
   data models in R.
   It provides functions to create, modify and export data models
   in json format.
   It also allows importing models created
   with 'MySQL Workbench' (<https://www.mysql.com/products/workbench/>).
   These functions are accessible through a graphical user
   interface made with 'shiny'.
   Constraints such as types, keys, uniqueness and mandatory fields are
   automatically checked and corrected when editing a model.
   Finally, real data can be confronted to a model to check their compatibility.