Performs Bayesian sample size, precision, and value-of-information analysis for external validation of existing multi-variable prediction models using the approach proposed by Sadatsafavi and colleagues (2026) <doi:10.1002/sim.70389>.

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.

Implements the Mixed Treatment-State Causal Model (MTSCM), a Bayesian 
    framework for estimating causal effects of clinical interventions on bounded 
    continuous outcomes in longitudinal observational studies with irregular visits. 
    The methodology is specifically designed for periodontal disease research, where 
    discrete treatments and continuous disease states (e.g., proportion of periodontal 
    pockets exceeding 3 mm) reciprocally influence one another under dynamic feedback. 
    The package integrates a double-censored Tobit likelihood to handle boundary mass 
    at zero and one, subject-specific random effects to capture within-subject 
    correlation, and flexible tree-based ensemble priors (standard BART and Soft BART) 
    to model complex nonlinear interactions without parametric restrictions. Causal 
    identification is established under the potential outcomes framework via the 
    G-computation formula, with key estimands including the Mixed Average Potential 
    Outcome (MAPO) and the Mixed Probability of Disease Resolution (MPDR). The 
    package provides functions for model fitting, posterior inference, and causal estimand 
    estimation.

Generating population projections for all countries of the world using several probabilistic components,
     such as total fertility rate, life expectancy at birth and net migration (Raftery et al., 2012 <doi:10.1073/pnas.1211452109>).
     The package can be also used for subnational population projections.

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'.