Enables a user to consume the 'BambooHR' API endpoints using R. The
  actual URL of the API will depend on your company domain, and will be handled
  by the package automatically once you setup the config file. The API documentation
  can be found here <https://documentation.bamboohr.com/docs>.

Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).

Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework.
  The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
  on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model.
  The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019)
  <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021)
  <doi:10.18637/jss.v100.i04>.

Species Distribution Modeling (SDM) is a practical methodology 
 that aims to estimate the area of distribution of a species. However, 
 most of the work has focused on estimating static expressions of the 
 correlation between environmental variables. The outputs of
 correlative species distribution models can be interpreted 
 as maps of the suitable environment for a species but not generally as maps
 of its actual distribution. 
 Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4> presented
 the BAM scheme, a heuristic framework that states that the occupied area of 
 a species occurs on sites that have been accessible through dispersal (M) and 
 have both favorable biotic (B) and abiotic conditions (A). 
 The 'bamm' package implements classes and functions to operate on each element 
 of the BAM and by using a cellular automata model where the occupied area 
 of a species at time t is estimated by the multiplication of three 
 binary matrices: one matrix represents movements (M), another 
 abiotic -niche- tolerances (A), and a third, biotic interactions (B). 
 The theoretical background of the package can be found in 
 Soberón and Osorio-Olvera (2022) <arXiv:2212.06308>.