Making probabilistic projections of total fertility rate for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-011-0040-5> <doi:10.18637/jss.v106.i08>. Subnational probabilistic projections are also supported <doi:10.4054/DemRes.2018.38.60>.

Fits joint Bayesian four-parameter logistic (4PL) models to
    thermal-tolerance proportion data, extracts the classical thermal load
    sensitivity quantities (z, CTmax at 1 hour, T_crit) with full posterior
    uncertainty, and predicts heat-injury accumulation and survival under
    fluctuating temperature regimes with optional Sharpe-Schoolfield repair.
    Models are fitted with 'Stan' via the 'brms' package. Implements the
    framework described in Noble, Arnold, Nakagawa and Pottier (in
    preparation).

Provides tools for conducting Bayesian analyses and Bayesian model averaging 
    (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, 
    Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains 
    functions for creating a wide range of prior distribution objects, mixing posterior 
    samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc...
    The tools for working with prior distribution span from visualization, generating 'JAGS' 
    and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.

This is an implementation of BART:Bayesian Additive Regression Trees,
        by Chipman, George, McCulloch (2010).

Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) <DOI:10.2307/2669832>. The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) <DOI:10.2307/2669832> is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016).

In screening programs, individuals are usually followed up and tested (screened) for the development of a disease, such as cancer. The target disease often develops progressively in stages; for example healthy (state 1), pre-state disease (state 2), and the disease state (state 3). When the pre-state disease is found during screening it is intervened upon, for example by surgical removal of a lesion, so that the progression of the pre-state disease to disease is interrupted. This is called censoring due to intervention. Researchers often want to estimate the time from baseline to the pre-state disease, the time from the pre-state disease to the disease, and the total time from baseline to the disease. In addition, researchers often want to regress these times on baseline covariates. To these ends, 'BayesTSM' estimates a progressive three-state model with censoring due to intervention using Bayesian estimation methods, as described in Klausch et al. (2023) <doi:10.1214/22-AOAS1669>.