Performs some basic models of Data Envelopment Analysis, both in multiplier and envelopment form.
This work has been partially supported by \emph{Ministerio de Educaci{\'o}n y Ciencia} of Spain through "Jos{\'e} Castillejo" scholarship obtained by Zuleyka D{\'i}az-Mart{\'i}nez.\newline C code that performs the Simplex algorithm comes from the GNU Linear Programming Kit v. 4.8, available at \url{http://www.gnu.org/software/glpk/}. Many thanks to its author Andrew Makhorin \email{mao@gnu.org}. \newline According to the usual practice in statistics, in this package each row in the data matrices containing the inputs and outputs corresponds to an observation and each column to a variable. They are the transposed of X and Y matrices used in Cooper, W. W., Seiford, L. M. and Tone, K.  (2007): \emph{Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software.} Second edition. Springer. New York.\newline This is a preliminar package. Any comments and suggestions are welcome.

This work has been partially supported by Ministerio de Educacion y Ciencia of Spain through "Jose Castillejo" scholarship obtained by Zuleyka Diaz Martinez. C code that performs the Simplex algorithm comes from the GNU Linear Programming Kit v. 4.8, available at http://www.gnu.org/software/glpk/. Many thanks to its author Andrew Makhorin (mao@gnu.org). According to the usual practice in statistics, in this package each row in the data matrices containing the inputs and outputs corresponds to an observation and each column to a variable. They are the transposed of X and Y matrices used in Cooper, W. W., Seiford, L. M. and Tone, K.  (2007): "Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software". Second edition. Springer. New York. This is a preliminar package. Any comments and suggestions are welcome.

person(given = "Brendan Matthew",
family = "Galdo",
role = c("aut", "cre"),
email = "Brendan.m.galdo@gmail.com",
comment = c(ORCID = "0000-0002-1279-3859"))
Bayesian inference algorithms based on the population-based "differential evolution" (DE) algorithm. Users can obtain posterior mode (MAP) estimates via DEMAP, posterior samples via DEMCMC, and variational approximations via DEVI.
Brendan Matthew Galdo [aut, cre]
(<https://orcid.org/0000-0002-1279-3859>)