The poems package provides a framework of interoperable R6 classes
(Chang, 2020, <https://CRAN.R-project.org/package=R6>) for building ensembles
of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,
2005, <doi:10.1126/science.1116681>). The package includes classes for
encapsulating and generating model parameters, and managing the POM workflow.
The workflow includes: model setup; generating model parameters via Latin
hyper-cube sampling (Iman & Conover, 1980, <doi:10.1080/03610928008827996>);
running multiple sampled model simulations; collating summary results; and
validating and selecting an ensemble of models that best match known patterns.
By default, model validation and selection utilizes an approximate Bayesian
computation (ABC) approach (Beaumont et al., 2002,
<doi:10.1093/genetics/162.4.2025>), although alternative user-defined
functionality could be employed. The package includes a spatially explicit
demographic population model simulation engine, which incorporates default
functionality for density dependence, correlated environmental stochasticity,
stage-based transitions, and distance-based dispersal. The user may customize
the simulator by defining functionality for translocations, harvesting,
mortality, and other processes, as well as defining the sequence order for the
simulator processes. The framework could also be adapted for use with other
model simulators by utilizing its extendable (inheritable) base classes.

c(
person("Sean", "Haythorne", email = "sean.haythorne@unimelb.edu.au", role = c("aut", "cre")),
person("Damien", "Fordham", email = "damien.fordham@adelaide.edu.au", role = "aut"),
person("Stuart", "Brown", email = "s.brown@adelaide.edu.au", role = "aut"),
person("Jessie", "Buettel", email = "jessie.buettel@utas.edu.au", role = "aut"),
person("Barry", "Brook", email = "barry.brook@utas.edu.au", role = "aut"))

Bayesian Variable Selection for a Poisson-Logistic Model
c(person("Michaela", "Dvorzak", role = c("aut","cre"),
email = "m.dvorzak@gmx.at"),
person("Helga", "Wagner", role = "aut"))
Michaela Dvorzak [aut, cre], Helga Wagner [aut]
Bayesian variable selection for regression models of under-reported
count data as well as for Poisson and binomial logit regression models using
spike and slab priors.