Presence-Only data is best modelled with a Point Process Model.
    The work of Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569> provides
    a way to use exact Bayesian inference to model this type of data, which is
    implemented in this package.

Generating population projections for all countries of the world using several probabilistic components, such as total fertility rate and life expectancy (Raftery et al., 2012 <doi:10.1073/pnas.1211452109>).

An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 <doi:10.2139/ssrn.2765419>), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) <doi:10.1111/j.1540-5907.2012.00602.x>, the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>). This package can be used with MCMC output generated by any Bayesian estimation tool including 'JAGS', 'BUGS', 'MCMCpack', and 'Stan'.

The goal of 'BayesPower' is to provide tools for Bayesian sample size determination and power analysis across a range of common hypothesis testing scenarios using Bayes factors. The main function, BayesPower_BayesFactor(), launches an interactive 'shiny' application for performing these analyses. The application also provides command-line code for reproducibility. Details of the methods are described in the tutorial by Wong, Pawel, and Tendeiro (2025) <doi:10.31234/osf.io/pgdac_v1>.

Bayesian power/type I error calculation and model fitting using 
  the power prior and the normalized power prior for generalized linear models.
  Detailed examples of applying the package are available at <doi:10.32614/RJ-2023-016>.
  Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'.
  The Bayesian clinical trial design methodology is described in Chen et al. (2011) 
  <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) 
  <doi:10.1093/biostatistics/kxy009>. The normalized power prior is described in Duan et al. (2006) 
  <doi:10.1002/env.752> and Ibrahim et al. (2015) <doi:10.1002/sim.6728>.