LinkingTo: Rcpp, RcppArmadillo (>= 0.4.100.2.1)

Title: Estimation of Hawkes Processes from Binned Observations
Description: Implements an estimation method for Hawkes processes when count data are only observed in discrete time, using a spectral approach derived from the Bartlett spectrum, see Cheysson and Lang (2020) <arXiv:2003.04314>. Some general use functions for Hawkes processes are also included: simulation of (in)homogeneous Hawkes process, maximum likelihood estimation, residual analysis, etc.
Author: Felix Cheysson [aut, cre] (<https://orcid.org/0000-0002-9095-2878>)

Title: Identifying Foggy and Cloudy Images by Quantifying Hazeness
Description: Provides a set of functions to estimate haziness of an image based on RGB bands. It returns a haze factor, varying from 0 to 1, a metric for fogginess and cloudiness. The package also presents additional functions to estimate brightness, darkness and contrast rasters of the RGB image. This package can be used for several applications such as inference of weather quality data and performing environmental studies from interpreting digital images.

Title: Identifying Foggy and Cloudy Images by Quantifying Haziness
Suggests: jpeg, testthat, knitr, rmarkdown

Title: Damage functions from FEMA's HAZUS software for use in modeling
Description: Damage Functions (DFs), also known as
Vulnerability Functions, associate the physical damage
to a building or a structure (and also its contents and
inventory) from natural disasters to financial damage.
The Federal Emergency Management Agency (FEMA) in USA
developed several thousand DFs and these serve as a
benchmark in natural catastrophe modeling, both in
academia and industry. However, these DFs and their
documentation are buried within the HAZUS software are
not easily accessible for analysis and visualization.
This package provides more than 1300 raw DFs used by FEMA's
HAZUS software and also functionality to extract and
visualize DFs specific to the flood hazard. The vignette
included with this package demonstrates its use.