c(
person("Eva", "Rifà", , "eva.rifarovira@bsc.es", role = c("cre")),
person("Nuria", "Perez-Zanon", , "nuria.perez@bsc.es", role = c("aut"), comment = c(ORCID = "0000-0001-8568-3071")),
person("Chou", "Chihchung", ,"chihchung.chou@bsc.es", role = "aut"),
person("Llorenç", "Lledó", , "llorenc.lledo@bsc.es", role = "aut"),
person("González-Reviriego", "Nube", , "nube.gonzalez@bsc.es", role = "ctb"),
person("Marcos", "Raül", , "raul.marcos@bsc.es", role = "ctb"),
person("Palma", "Lluis", , "lluis.palma@bsc.es", role = "ctb"),
person("An-Chi", "Ho", , "an.ho@bsc.es", role = c("ctb")),
person("BSC-CNS", role = "cph"))
Set of generalised tools for the flexible computation of climate
related indicators defined by the user. Each method represents a specific
mathematical approach which is combined with the possibility to select an
arbitrary time period to define the indicator. This enables a wide range of
possibilities to tailor the most suitable indicator for each particular climate
service application (agriculture, food security, energy, water management…).
This package is intended for sub-seasonal, seasonal and decadal climate
predictions, but its methods are also applicable to other time-scales,
provided the dimensional structure of the input is maintained. Additionally,
the outputs of the functions in this package are compatible with 'CSTools'.
This package was developed in the context of H2020 MED-GOLD (776467) and
S2S4E (776787) projects. Lledó et al. (2019) <doi:10.1016/j.renene.2019.04.135>.
Eva Rifà [cre],
Nuria Perez-Zanon [aut] (<https://orcid.org/0000-0001-8568-3071>),
Chou Chihchung [aut],
Llorenç Lledó [aut],
González-Reviriego Nube [ctb],
Marcos Raül [ctb],
Palma Lluis [ctb],
An-Chi Ho [ctb],
BSC-CNS [cph]

Cost-Sensitive Multi-Criteria Ensemble Selection for Uncertain
Cost Conditions
Koen W. De Bock, Kristof Coussement and Stefan Lessmann
Functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) <doi:10.1016/j.ejor.2020.01.052>) for cost-sensitive learning under unknown cost conditions.