rjson, RCurl, soiltexture, spatstat, stringr, XML, boot,
splines, mda, psych, nortest, rpart, quantregForest,
randomForest, nlme, reshape, fossil, AICcmodavg, maptools,
nnet, SDMTools, spacetime, gdalUtils, tools, maxlike, Hmisc,
caret, xgboost, ranger, snowfall

c(person("Tomislav", "Hengl", role = c("cre", "aut"), email = "tom.hengl@opengeohub.org"),
person("Bas", "Kempen", role = "ctb", email = "bas.kempen@wur.nl"),
person("Gerard", "Heuvelink", role = "ctb", email = "gerard.heuvelink@wur.nl"),
person("Brendan", "Malone", role = "ctb", email = "brendan.malone@scsiro.au"))

This package implements a Bayesian approach for estimation of a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter.

This package implements a Bayesian approach for estimation
of a mixture of gamma distributions in which the mixing occurs
over the shape parameter. This family provides a flexible and
novel approach for modeling heavy-tailed distributions, it is
computationally efficient, and it only requires to specify a
prior distribution for a single parameter.

This package implements a Bayesian approach for estimation of a mixture of gamma distributions in which the mixing occurs over the shape parameter.
This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify
a prior distribution for a single parameter.

Implementation of a Bayesian approach for estimating a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter.

This package implements a Bayesian approach for estimation
of a mixture of gamma distributions in which the mixing occurs
over the shape parameter.  This family provides a flexible and
novel approach for modeling heavy-tailed distributions, it is
computationally efficient, and it only requires to specify a
prior distribution for a single parameter.