Title: Decision Models with Multi Attribute Utility Theory
Author: Felipe Aguirre, Julio Andrade, Pedro Guarderas, Daniel Lagos,
Andres Lopez, Nelson Recalde, Edison Salazar.
Description: Build and test decision models based in Multi Attribute Utility Theory (MAUT).
Automatic evaluation of utilities at any level of the decision tree, weight simulations for
Imports: data.table, gtools, stringr, igraph, RColorBrewer, ggplot2

Description: Provides functions for the creation, evaluation and test of decision models based in
Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities
for a set of indexes, compute utilities and weights for the whole decision tree defining the
decision model and simulate weights employing Dirichlet distributions under addition constraints
Imports: data.table, gtools, stringr, igraph, RColorBrewer, ggplot2,

'BEAST2' is commonly accompanied by 'BEAUti 2' (<http://www.beast2.org>),
which, among others, allows one to install 'BEAST2' package.
This package allows to install 'BEAST2' packages from 'R'.
Suggests: testit, testthat (>= 2.1.0), knitr, rmarkdown

Suggests: beautier, curl, knitr, rmarkdown, testit, testthat (>= 2.1.0)

'BEAST2' is commonly accompanied by 'BEAUti 2' (<https://www.beast2.org>),
Suggests: beautier, knitr, rmarkdown, testit, testthat (>= 2.1.0)

This package allows to work with 'BEAST2' packages from 'R'.
Suggests: beautier (>= 2.6.2), curl, knitr, markdown, rmarkdown,

Description: Tools for Maximum Likelihood Estimation
Packaged: Sun Jan 25 22:43:03 2009; suapm095

Author: Ott Toomet <otoomet@ut.ee>, Arne Henningsen

Packaged: Mon Feb 25 15:57:06 2008; suapm095

Packaged: Wed Sep 17 11:21:08 2008; suapm095

Packaged: Thu Sep 18 10:37:17 2008; suapm095

Author: Ott Toomet <otoomet@gmail.com>, Arne Henningsen

with contributions from Spencer Graves and Yves Croissant

Title: Maximum Likelihood Estimation and Related Tools
Depends: R (>= 2.4.0), miscTools (>= 0.6-8), methods
Description: Functions for Maximum Likelihood (ML) estimation and non-linear
optimization, and related tools.  It includes a unified way to call
different optimizers, and classes and methods to handle the results from
the ML viewpoint.  It also includes a number of convenience tools for testing