person("Thomas", "Nagler",, "mail@tnagler.com", role = c("aut", "cre")),
Imports: graphics, grDevices, stats, utils, MASS, mvtnorm, methods,
Suggests: CDVine, TSP, shiny, testthat, numDeriv, kdecopula (>= 0.8.0),

Suggests: TSP, shiny, testthat, numDeriv, kdecopula (>= 0.8.0), network

Description: Provides tools for the statistical analysis of regular vine copula
models, see Aas et al. (2009) <doi:10.1016/j.insmatheco.2007.02.001> and

Title: Simulation of Populations by Sampling Waiting-Time Distributions
Authors@R: c(person("Frans", "Willekens", , "willekens@nidi.nl", role = c("aut", "cre"),
email = "tim.riffe@gmail.com", comment = c(ORCID = "0000-0002-2673-4622")))
Description: Generates lifespans and fertility histories in continuous time using individual-level state transition (multi-state) models and data from the Human Mortality Database and the Human Fertility Database. To facilitate virtual population analysis, data on virtual individuals are stored in a data structure commonly used in sample surveys. Life histories are generated for multiple generations. The genealogies that result facilitate the study of family ties.
Author: Frans Willekens [aut, cre] (<https://orcid.org/0000-0001-6125-0212>),

Depends: R (>= 2.12.0), bayesm, MASS, clusterGeneration, TeachingDemos,
Description: Visualization of distributions of covariance matrices

Title: Visualizing of distributions of covariance matrices
Author: Tomoki Tokuda <tomoki.tokuda@psy.kuleuven.be>; Ben Goodrich
Maintainer: Tomoki Tokuda <tomoki.tokuda@psy.kuleuven.be>; Francis
Description: Visualizing of distributions of covariance matrices

Title: Modeling and Forecasting Visitor Counts Using Social Media
person("Russell", "Goebel", email = "rgoebel@bu.edu", role = "aut"),
person("Robert", "Bowen", email = "robertbowen.bham@gmail.com", role = c("aut", "cre")),
person("Beth Ann", "Brackett", email = "eliannkb@gmail.com", role = "ctb"),
person("Kimihiro", "Noguchi", email = "Kimihiro.noguchi@wwu.edu", role = "aut")
Description: Performs modeling and forecasting of park visitor counts
using social media data and (partial) on-site visitor counts.
Specifically, the model is built based on an automatic decomposition
of the trend and seasonal components of the social media-based park visitor counts,
from which short-term forecasts of the visitor counts and percent changes
in the visitor counts can be made. A reference for generating social media-based
Wood, Guerry, Silver, and Lacayo (2013) <doi:10.1038/srep02976>.
Imports: Rssa, ggplot2, ggthemes, tibble, methods