Depends: QRMlib, compiler, R (>= 2.13.0)

Depends: MASS, fUtilities, compiler, R (>= 2.13.0)
Description: Performs general Metropolis-Hastings Markov Chain Monte Carlo sampling of a user defined function which returns the un-normalized value (likelihood times prior) of a Bayesian model.  The proposal variance-covariance structure is updated adaptively for efficient mixing when the structure of the target distribution is unknown.  The package also provides some functions for Bayesian inference including Bayesian Credible Intervals (BCI) and Deviance Information Criterion (DIC) calculation.

Title: Maxent Distribution Model Selection
person("Julien", "Vollering", email = "julien.vollering@hisf.no", role = c("aut", "cre")),
person("Sabrina", "Mazzoni", email = "sabrina.mazzoni@nhm.uio.no", role = "aut"),
person("Rune", "Halvorsen", email = "rune.halvorsen@nhm.uio.no", role = "aut"))
Description: Tools for training, selecting, and evaluating maximum entropy
(Maxent) distribution models. This package provides tools for user-
controlled transformation of explanatory variables, selection of variables
by nested model comparison,  and flexible model evaluation and projection.
It is based on the strict maximum likelihood interpretation of maximum
SystemRequirements: Maxent.jar software, Java Runtime Environment (JRE)

Imports: dplyr (>= 0.4.3), e1071 (>= 1.6-7), graphics, grDevices, Hmisc

person("Julien", "Vollering", email = "julien.vollering@hvl.no", role = c("aut", "cre")),
person("Sabrina", "Mazzoni", email = "sabgis@uevora.pt", role = "aut"),
Imports: dplyr (>= 0.4.3), e1071 (>= 1.6-7), graphics, grDevices,

Title: A Modular, Integrated Approach to Maximum Entropy Distribution
person("Rune", "Halvorsen", email = "rune.halvorsen@nhm.uio.no", role = "aut"),
person("Steven", "Phillips", role = "cph"))
(and standard logistic regression) distribution models. This package
provides tools for user-controlled transformation of explanatory variables,
selection of variables by nested model comparison, and flexible model
evaluation and projection. It follows principles based on the maximum-
likelihood interpretation of maximum entropy modeling, and uses infinitely-
weighted logistic regression for model fitting.
Packaged: 2018-11-13 22:58:12 UTC; julienvollering