Authors@R: c(person("Jamie", "Lentin", email="lentinj@shuttlethread.com",role = c("aut", "cre", "cph")),
person("Bjarki Thor", "Elvarsson", email="bjarki.elvarsson@hafogvatn.is",role = c("aut")))
Description: Creates and manages a PostgreSQL database suitable for storing fisheries data
and aggregating ready for use within a Gadget <https://hafro.github.io/gadget> model.
See <https://mareframe.github.io/mfdb> for more information.

and aggregating ready for use within a Gadget <https://gadget-framework.github.io/gadget2/> model.
See <https://mareframe.github.io/mfdb/> for more information.
Suggests: dplyr (>= 0.8.3), dbplyr (>= 2.0.0), knitr, rmarkdown,

role=c("aut", "cre")), person("Luis Paulo", "F. Garcia",
"C. P. L. F. de Carvalho", email="andre@icmc.usp.br", role="ths"))
Description: Extracts meta-features from datasets to support the design of
recommendation systems based on Meta-Learning. The meta-features, also
called characterization measures, are able to characterize the complexity of
datasets and to provide estimates of algorithm performance. The package
contains not only the standard characterization measures, but also more
recent characterization measures. By making available a large set of
meta-feature extraction functions, this package allows a comprehensive data
characterization, a deep data exploration and a large number of
Meta-Learning based data analysis. These concepts are described in the book:
Brazdil, P., Giraud-Carrier, C., Soares, C., Vilalta, R. (2009)
Imports: C50, class, e1071, infotheo, MASS, rpart, stats, utils

role=c("aut", "cre")), person("Luis", "P. F. Garcia",
recommendation systems based on Meta-Learning. The meta-features, also called
characterization measures, are able to characterize the complexity of datasets
and to provide estimates of algorithm performance. The package contains not
only the standard characterization measures, but also more recent
characterization measures. By making available a large set of meta-feature
extraction functions, tasks like comprehensive data characterization, deep
data exploration and large number of Meta-Learning based data analysis can be
performed. These concepts are described in the paper: Fabio Pinto, Carlos
Soares, and Joao Mendes-Moreira. Towards automatic generation of metafeatures.
In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD),
Imports: cluster, e1071, infotheo, MASS, rpart, rrcov, stats, utils