c(person("Øyvind", "Langsrud", role = c("aut", "cre"),
email = "oyl@ssb.no"),
person("Daniel", "Lupp", role = c("aut")),
person("Hege", "Bøvelstad", role = c("ctb")))
Øyvind Langsrud [aut, cre],
Daniel Lupp [aut],
Hege Bøvelstad [ctb]

SSBtools (>= 1.3.4), RegSDC (>= 0.7.0), stats, methods

Inference, Goodness-of-Fit and Forecast for Univariate Gaussian
Hidden Markov Models
Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.

Dimitrina S. Dimitrova <D.Dimitrova@city.ac.uk>,
Vladimir K. Kaishev <V.Kaishev@city.ac.uk>,
Andrea Lattuada <Andrea.Lattuada@unicatt.it> and
Richard J. Verrall <R.J.Verrall@city.ac.uk>
Geometrically Designed Spline ('GeDS') Regression is a non-parametric geometrically
motivated method for fitting variable knots spline predictor models in one or two independent
variables, in the context of generalized (non-)linear models. 'GeDS' estimates the number and
position of the knots and the order of the spline, assuming the response variable has a
distribution from the exponential family. A description of the method can be found in
Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2017) <https://openaccess.city.ac.uk/18460>.
R (>= 3.0.1), Rcpp (>= 0.12.1), splines, stats, utils, Matrix,
methods, Rmpfr

Spatial Graph-Theoretic Genetic Gravity Modelling
c(person(family="Evans", given="Jeffrey S.", email = "jeffrey_evans@tnc.org",
role = c("aut", "cre")), person(family="Murphy", given="Melanie", role = "aut") )
Implementation of spatial graph-theoretic genetic gravity models.
The model framework is applicable for other types of spatial flow questions.
Includes functions for constructing spatial graphs, sampling and summarizing
associated raster variables and building unconstrained and singly constrained
gravity models.
nlme, sp, spdep, spatialEco, raster, rgeos
Jeffrey S. Evans [aut, cre],
Melanie Murphy [aut]