Bayesian Inference of Landscape Occupancy from Presence-Only
Data
c(
person(given = "Camilo", family= "Sanin", email= "camilosanin@gmail.com", role = c("aut", "cre", "cph")),
person(given = "Adam B.", family= "Smith", email= "adam.smith@mobot.org", role = c("aut", "cph")),
person("Missouri Botanical Garden", role = "cph"),
person("Trustees of Columbia University", role = "cph")
)
Natural history museums and herbaria collectively hold hundreds of millions of zoological, botanical, and paleontological specimens. These collections serve as the foundation for understanding the distribution of life on Earth and the basis for addressing loss of biodiversity, emerging diseases, and other pressing global problems as well as important question in ecology and evolution. One of the short comings of these kind of data is that the lack of evidence of the presence of a species in a certain region does not mean the species is truly absent there. Likewise, specimens are often misidentified, and therefore the report of a species in a locality is not always evidence that a viable population occurs there. The goal of this project is to develop a method which could be used to estimate the probability of presence of a species in a certain study region based on certain sampling effort and presence reports.
Camilo Sanin [aut, cre, cph],
Adam B. Smith [aut, cph],
Missouri Botanical Garden [cph],
Trustees of Columbia University [cph]

Mixtures-of-Experts Markov Chain Clustering and Dirichlet
Multinomial Clustering
This package provides various Markov Chain Monte Carlo
(MCMC) sampler for model-based clustering of discrete-valued
time series obtained by observing a categorical variable with
several states (in a Bayesian approach).  In order to analyze
group membership, we provide also an extension to the
approaches by formulating a probabilistic model for the latent
group indicators within the Bayesian classification rule using
a multinomial logit model.
R (>= 2.14.1), gplots, xtable, grDevices, mnormt, MASS,
bayesm, boa, e1071, gtools