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.

The network autocorrelation model (NAM) can be used for studying the degree of social influence 
    regarding an outcome variable based on one or more known networks. 
    The degree of social influence is quantified via the network autocorrelation parameters. In case of a single
    network, the Bayesian methods of Dittrich, Leenders, and Mulder
    (2017) <DOI:10.1016/j.socnet.2016.09.002> and Dittrich, Leenders, and Mulder (2019)
    <DOI:10.1177/0049124117729712> are implemented using a normal, flat, or independence  
    Jeffreys prior for the network autocorrelation. In the case of multiple 
    networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020) 
    <DOI:10.1177/0081175020913899> are implemented using a multivariate normal prior for 
    the network autocorrelation parameters. Flat priors are implemented 
    for estimating the coefficients. For Bayesian testing of equality and order-constrained 
    hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018) 
    <DOI:10.1111/bmsp.12110> is used with the posterior mean and posterior covariance 
    matrix of the NAM parameters based on flat priors as input.