c(
person("Eric", "Marcon", email="eric.marcon@ecofog.gf", role=c("aut", "cre"), comment = c(ORCID = "0000-0002-5249-321X")),
person("Gabriel", "Lang", email="gabriel.lang@agroparistech.fr", role="aut", comment = c(ORCID = "0000-0002-4325-6044")),
person("Stephane", "Traissac", email="stephane.traissac@agroparistech.fr", role="aut"),
person("Florence", "Puech", email="florence.puech@u-psud.fr", role="aut", comment = c(ORCID = "0000-0002-5279-6878"))
)
cubature, reshape2, stats, RcppParallel, spatstat.utils (>=
1.3-1)
Eric Marcon [aut, cre] (<https://orcid.org/0000-0002-5249-321X>),
Gabriel Lang [aut] (<https://orcid.org/0000-0002-4325-6044>),
Stephane Traissac [aut],
Florence Puech [aut] (<https://orcid.org/0000-0002-5279-6878>)

cubature, reshape2, stats, RcppParallel, spatstat.utils (>=
1.3-1), tibble

c(
person("Eric", "Marcon", email="eric.marcon@agroparistech.fr", role=c("aut", "cre"), comment = c(ORCID = "0000-0002-5249-321X")),
person("Gabriel", "Lang", email="gabriel.lang@agroparistech.fr", role="aut", comment = c(ORCID = "0000-0002-4325-6044")),
person("Stephane", "Traissac", email="stephane.traissac@agroparistech.fr", role="aut", comment = c(ORCID = "0000-0001-9255-1616")),
person("Florence", "Puech", email="florence.puech@u-psud.fr", role="aut", comment = c(ORCID = "0000-0002-5279-6878"))
)
cubature, ggplot2, RcppParallel, reshape2, rlang,
spatstat.utils, stats, tibble, spatstat.geom, spatstat.random

cubature, reshape2, stats, RcppParallel, spatstat.utils,
tibble

R (>= 3.5.0), spatstat.core, spatstat.geom, Rcpp (>= 0.12.14),
ggplot2

cubature, RcppParallel, reshape2, spatstat.utils, stats,
tibble

cubature, RcppParallel, reshape2, spatstat.utils, stats,
tibble, spatstat.geom, spatstat.random

Dynamic Bayesian Network Learning and Inference
c(
person("David", "Quesada", email = "dkesada@gmail.com", role = c("aut", "cre")),
person("Gabriel", "Valverde", email = "gabrielvalverdecastilla@gmail.com", role = "ctb"))
Learning and inference over dynamic Bayesian networks of arbitrary
Markovian order.  Extends some of the functionality offered by the 'bnlearn'
package to learn the networks from data and perform exact inference.
It offers a modification of Trabelsi (2013) <doi:10.1007/978-3-642-41398-8_34>
dynamic max-min hill climbing algorithm for structure learning and
the possibility to perform forecasts of arbitrary length. A tool for
visualizing the structure of the net is also provided via the 'visNetwork' package.
David Quesada [aut, cre],
Gabriel Valverde [ctb]