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>.

Provides functions for analyzing and visualizing complex
    macroevolutionary dynamics on phylogenetic trees. It is a companion
    package to the command line program BAMM (Bayesian Analysis of
    Macroevolutionary Mixtures) and is entirely oriented towards the analysis,
    interpretation, and visualization of evolutionary rates. Functionality
    includes visualization of rate shifts on phylogenies, estimating
    evolutionary rates through time, comparing posterior distributions of
    evolutionary rates across clades, comparing diversification models using
    Bayes factors, and more.

Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R.