Native R Kernel for the 'Jupyter Notebook'
The R kernel for the 'Jupyter' environment executes R code which the front-end
('Jupyter Notebook' or other front-ends) submits to the kernel via the network.
c(
person('Thomas', 'Kluyver', role = c('aut', 'cph'), email = 'thomas@kluyver.me.uk'),
person('Philipp', 'Angerer', role = c('aut', 'cph', 'cre'), email = 'phil.angerer@gmail.com'),
person('Jan', 'Schulz', role = c('aut', 'cph'), email = 'jasc@gmx.net'),
person('Karthik', 'Ram', role = c('aut', 'cph'), email = 'karthik.ram@gmail.com'))
jupyter, jupyter_kernel_test (Python package for
testing)
repr (>= 0.4.99), methods, evaluate (>= 0.10), IRdisplay (>=
0.3.0.9999), pbdZMQ (>= 0.2-1), crayon, jsonlite (>= 0.9.6),
uuid, digest
'class_unions.r' 'logging.r' 'comm_manager.r' 'compat.r'
'environment_runtime.r' 'environment_shadow.r' 'options.r'
'execution.r' 'handlers.r' 'help.r' 'installspec.r' 'utils.r'
'kernel.r' 'main.r' 'onload.r'
Thomas Kluyver [aut, cph],
Philipp Angerer [aut, cph, cre],
Jan Schulz [aut, cph],
Karthik Ram [aut, cph]

c(
person('Thomas', 'Kluyver', role = c('aut', 'cph'), email = 'thomas@kluyver.me.uk'),
person('Philipp', 'Angerer', role = c('aut', 'cph', 'cre'), email = 'phil.angerer@gmail.com', comment = c(ORCID = "0000-0002-0369-2888")),
person('Jan', 'Schulz', role = c('aut', 'cph'), email = 'jasc@gmx.net'),
person('Karthik', 'Ram', role = c('aut', 'cph'), email = 'karthik.ram@gmail.com'))
Thomas Kluyver [aut, cph],
Philipp Angerer [aut, cph, cre]
(<https://orcid.org/0000-0002-0369-2888>),
Jan Schulz [aut, cph],
Karthik Ram [aut, cph]

'class_unions.r' 'logging.r' 'comm_manager.r' 'compat.r'
'completion.r' 'environment_runtime.r' 'environment_shadow.r'
'options.r' 'execution.r' 'handlers.r' 'help.r' 'installspec.r'
'utils.r' 'kernel.r' 'main.r' 'onload.r'

c(person("Nuno", "Moniz", email = "nmmoniz@inesctec.pt",
role = c("cre","aut")),
person("Rita", "P. Ribeiro", email = "rpribeiro@dcc.fc.up.pt",
role = "aut"),
person("Miguel", "Margarido", email = "miguelmargarido@gmail.com",
role = "ctb"))
Imbalanced domain learning has almost exclusively focused on solving
classification tasks, where the objective is to predict cases labelled with a
rare class accurately. Such a well-defined approach for regression tasks lacked
due to two main factors. First, standard regression tasks assume that each value
is equally important to the user. Second, standard evaluation metrics focus on
assessing the performance of the model on the most common cases. This package
contains methods to tackle imbalanced domain learning problems in regression
tasks, where the objective is to predict extreme (rare) values.
The methods contained in this package are: 1) an automatic and non-parametric
method to obtain such relevance functions; 2) visualisation tools; 3) suite of
evaluation measures for optimisation/validation processes; 4) the squared-error
relevance area measure, an evaluation metric tailored for imbalanced regression tasks.
More information can be found in Ribeiro and Moniz (2020) <doi:10.1007/s10994-020-05900-9>.
Rcpp, stats, ggpubr, gridExtra, ggplot2, robustbase, plot3D,
fields, scam
rpart, e1071, earth, randomForest, mgcv, reshape
Nuno Moniz [cre, aut],
Rita P. Ribeiro [aut],
Miguel Margarido [ctb]