Estimate Migration Birds Flyways Network with HSMM
c(
person(given="Marie-Josée", family="Cros", email = "Marie-Josee.Cros@inrae.fr", role = c("aut", "cre")),
person(given="Sam", family="Nicol", email = "Sam.Nicol@csiro.au", role = "aut"),
person(given="Nathalie", family="Peyrard", email = "Nathalie.Peyrard@inrae.fr",  role = "aut"),
person(given="Régis", family="Sabbadin", email = "Regis.Sabbadin@inrae.fr", role = "aut"),
person(given="Ronan", family="Trépos", email = "Ronan.Trepos@inrae.fr", role = "aut") )
Estimate migration birds flyways network from counts with
a Hidden Semi-Markov Model (HSMM). Implements several methods to estimate parameters:
Monte Carlo Expectation–Maximization (MCEM) and Approximate Bayesian Approach (ABC).
utils, stats, graphics, igraph, Rcpp, EasyABC, modeest
Marie-Josée Cros [aut, cre],
Sam Nicol [aut],
Nathalie Peyrard [aut],
Régis Sabbadin [aut],
Ronan Trépos [aut]

c(person(given = "Daniele",
family = "Girolimetto",
role = c("aut", "cre", "fnd"),
email = "daniele.girolimetto@studenti.unipd.it"),
person(given = "Tommaso",
family = "Di Fonzo",
role = c("aut", "fnd")))
Provides classical (bottom-up), optimal and heuristic combination
forecast reconciliation procedures for cross-sectional, temporal, and
cross-temporal linearly constrained time series.
Daniele Girolimetto [aut, cre, fnd],
Tommaso Di Fonzo [aut, fnd]

c(person(given = "Daniele",
family = "Girolimetto",
role = c("aut", "cre", "fnd"),
email = "daniele.girolimetto@phd.unipd.it"),
person(given = "Tommaso",
family = "Di Fonzo",
role = c("aut", "fnd")))

Classical (bottom-up and top-down), optimal and heuristic combination forecast
reconciliation procedures for cross-sectional, temporal, and cross-temporal linearly
constrained time series.

Classical (bottom-up and top-down), optimal and heuristic combination forecast
reconciliation procedures for cross-sectional, temporal, and cross-temporal linearly
constrained time series (Di Fonzo and Girolimetto, 2021, <doi:10.1016/j.ijforecast.2021.08.004>).