Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.

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
    (2023) <doi:10.1111/jbi.14587>.