Title: Efficient Authenticated Interaction with the O*NET API

Packaged: 2014-09-12 23:17:17 UTC; ericknudsen

Description: Provides a function to calculate the out-of-bag learning curve for random forests for any measure that is available in the 'mlr' package. Supported random forest packages are 'randomForest' and 'ranger' and trained models of these packages with the train function of 'mlr'.
Authors@R: person("Philipp", "Probst", email = "philipp_probst@gmx.de", role = c("aut", "cre"))

Description: Provides functions to calculate the out-of-bag learning curve for random forests for any measure that is available in the 'mlr' package. Supported random forest packages are 'randomForest' and 'ranger' and trained models of these packages with the train function of 'mlr'. The main function is 'OOBCurve' that calculates the out-of-bag curve depending on the number of trees. With the 'OOBCurvePars' function out-of-bag curves can also be calculated for 'mtry', 'sample.fraction' and 'min.node.size' for the 'ranger' package.

Description: Provides functions to calculate the out-of-bag learning curve for random forests for any measure that is available in the 'mlr' package. Supported random forest packages are 'randomForest' and 'ranger' and trained models of these packages with the train function of 'mlr'. The main function is OOBCurve() that calculates the out-of-bag curve depending on the number of trees. With the OOBCurvePars() function out-of-bag curves can also be calculated for 'mtry', 'sample.fraction' and 'min.node.size' for the 'ranger' package.

person("Elad", "Guttman", role = c("aut", "cre"), email = "eladguttman@mail.tau.ac.il"),
person("Oren", "Danieli", role = "aut"))
Calculates the Outside Option Index proposed by Caldwell and Danieli
This index uses the cross- sectional concentration of similar workers
across job types to quantify the availability of outside options as a function of
workers’ characteristics (e.g. commuting costs, preferences, and skills.)

person("M.", "Binois", role = c("cre", "aut", "trl"), comment = "R port", email = "mickael.binois@chicagobooth.edu"),
person("A.", "Carpentier", role = "aut", comment = "Matlab original"),
person("J.-B.", "Grill", role = "aut", comment = "Python original"),
person("R.", "Munos", role = "aut", comment = "Python and Matlab original"),
person("M.", "Valko", role = c("aut", "ctb"), comment = "Python and Matlab original"))
Description: Implementation of optimistic optimization methods for global optimization of deterministic or stochastic functions. The algorithms feature guarantees of the convergence to a global optimum. They require minimal assumptions on the (only local) smoothness, where the smoothness parameter does not need to be known. They are expected to be useful for the most difficult functions when we have no information on smoothness and the gradients are unknown or do not exist. Due to the weak assumptions, however, they can be mostly effective only in small dimensions, for example, for hyperparameter tuning.
Author: M. Binois [cre, aut, trl] (R port),
R. Munos [aut] (Python and Matlab original),
M. Valko [aut, ctb] (Python and Matlab original)