Fully Bayesian inference for estimating the number of clusters and related parameters to heterogeneous binary data.

A hodgepodge of hopefully helpful functions. Two of these perform
    shrinkage estimation: one using a simple weighted method where the user can
    specify the degree of shrinkage required, and one using James-Stein shrinkage
    estimation for the case of unequal variances.

Functions for performing the Bayesian bootstrap as introduced by
    Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the result.
    The implementation can handle both summary statistics that works on a
    weighted version of the data and summary statistics that works on a
    resampled data set.

Smoothed lexis diagrams with Bayesian method specifically tailored to cancer 
             incidence data. Providing to calculating slope and constructing credible interval.
             LC Chien et al. (2015) <doi:10.1080/01621459.2015.1042106>. 
             LH Chien et al. (2017) <doi:10.1002/cam4.1102>.

Performs CACE (Complier Average Causal Effect analysis) on either a single study or meta-analysis of datasets with binary outcomes, using either complete or incomplete noncompliance information. Our package implements the Bayesian methods proposed in Zhou et al. (2019) <doi:10.1111/biom.13028>, which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) <doi:10.1080/01621459.2021.1900859>, with an application example on Epidural Analgesia.

Combine diverse evidence across multiple studies to test a high level scientific theory. The methods can also be used as an alternative to a standard meta-analysis.

Bayesian multivariate binary (probit) regression
    models for analysis of ecological communities.

Tools for Bayesian copula generalized linear models (GLMs). 
             The sampling scheme is based on Pitt, Chan, and Kohn (2006) <doi:10.1093/biomet/93.3.537>. 
             Regression parameters (including coefficients and dispersion parameters) are
             estimated via the adaptive random walk Metropolis approach developed by
             Haario, Saksman, and Tamminen (1999) <doi:10.1007/s001800050022>.
             The prior for the correlation matrix is based on Hoff (2007) <doi:10.1214/07-AOAS107>.