mau-package: mau
In pedroguarderas/mau: Decision Models with Multi Attribute Utility Theory
mau-package R Documentation
mau
Description
Provides functions for the creation, evaluation and test of decision models based in
Multi Attribute Utility Theory (MAUT).
Details
MAUT models are defined employing a decision tree where similarity relations between
different index utilities are defined, this helps to group utilities following a criteria of
similarity. Each final node has an utility and weight associated, the utility of any internal
node in the decision tree is computed by adding the weighted sum of eaf of its final nodes. In a
model with n indexes, a criteria is composed by C \subset \{1,\ldots,n\}, the
respective utility is given by:
\sum_{i \in C}^n w_i u_i( x_i )
Currently, each utility is defined like a piecewise risk aversion utility, those functions are
of the following form:
a x + b
or
a e^{cx} + b
The current capabilities of mau are:
Read a list of risk aversion utilities defined in a standardized format.
Evaluate utilities of a table of indexes.
Load decision trees defined in column standard format.
Compute criteria utilities and weights for any internal node of the decision tree.
Simulate weights employing Dirichlet distributions under addition constraints in weights.
Author(s)
Maintainer: Pedro Guarderas pedro.felipe.guarderas@gmail.com
Other contributors:
Felipe Aguirre [contributor]
Julio Andrade [contributor]
Daniel Lagos [contributor]
Andrés Lopez [contributor]
Nelson Recalde [contributor]
Edison Salazar [contributor]
References
\insertRefDecMakmau
\insertRefHarDecmau
\insertRefUtiThemau
\insertRefDecQua:1996mau
\insertRefDecRis:1992mau
See Also
Useful links:
Examples
library( mau )
vignette( topic = 'Running_MAUT', package = 'mau' )
pedroguarderas/mau documentation built on Oct. 30, 2023, 4:20 a.m.
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| mau-package | R Documentation |
mau
Description
Provides functions for the creation, evaluation and test of decision models based in Multi Attribute Utility Theory (MAUT).
Details
MAUT models are defined employing a decision tree where similarity relations between
different index utilities are defined, this helps to group utilities following a criteria of
similarity. Each final node has an utility and weight associated, the utility of any internal
node in the decision tree is computed by adding the weighted sum of eaf of its final nodes. In a
model with n indexes, a criteria is composed by C \subset \{1,\ldots,n\}, the
respective utility is given by:
\sum_{i \in C}^n w_i u_i( x_i )
Currently, each utility is defined like a piecewise risk aversion utility, those functions are of the following form:
a x + b
or
a e^{cx} + b
The current capabilities of mau are:
Read a list of risk aversion utilities defined in a standardized format.
Evaluate utilities of a table of indexes.
Load decision trees defined in column standard format.
Compute criteria utilities and weights for any internal node of the decision tree.
Simulate weights employing Dirichlet distributions under addition constraints in weights.
Author(s)
Maintainer: Pedro Guarderas pedro.felipe.guarderas@gmail.com
Other contributors:
Felipe Aguirre [contributor]
Julio Andrade [contributor]
Daniel Lagos [contributor]
Andrés Lopez [contributor]
Nelson Recalde [contributor]
Edison Salazar [contributor]
References
\insertRefDecMakmau
\insertRefHarDecmau
\insertRefUtiThemau
\insertRefDecQua:1996mau
\insertRefDecRis:1992mau
See Also
Useful links:
Examples
library( mau )
vignette( topic = 'Running_MAUT', package = 'mau' )
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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