Interactively exploring virtually any Bayesian model fit using a Markov chain Monte Carlo algorithm (shinystan)
Most applied Bayesian data analysis requires employing a Markov chain Monte Carlo (MCMC) algorithm to obtain samples from the posterior distributions of the quantities of interest. Diagnosing convergence, checking the fit of the model, and producing graphical and numerical summaries of the parameters of interest is an essential but often laborious process that slows the down the creative and exploratory process of model building. The shinyStan package and Shiny app is designed to facilitate this process in two primary ways:
• Providing interactive visual model exploration: shinyStan provides immediate, informative, customizable visual and numerical summaries of model parameters and convergence diagnostics for MCMC simulations. Although shinyStan has some special features only available for users of the Rstan package (the R interface to the Stan programming language for Bayesian statistical inference), it can also easily be used to explore the output from any other program (e.g. Jags, Bugs, SAS) or any user-written MCMC algorithm.,
• Making saving and sharing more convenient: shinyStan allows you to store the basic components of an entire project (code, posterior samples, graphs, tables, notes) in a single object. Users can also export graphics into their R sessions as ggplot2 objects for further customization and easy integration in reports or post-processing for publication.

Network Scale Up Method (NSUM)
A Bayesian framework for population group size estimation using the Network Scale Up Method (NSUM). Size estimates are based on a random degree model and include options to adjust for barrier and transmission effects.

Klimontovich’s S-Theorem Algorithm Implementation and Data Preparation Tools (stheoreme)
Functions implementing the procedure of entropy comparison between two data samples after the renormalization of respective probability distributions with the algorithm designed by Klimontovich (Zeitschrift fur Physik B Condensed Matter. 1987, Volume 66, Issue 1, pp 125-127) and extended by Anishchenko (Proc. SPIE 2098, Computer Simulation in Nonlinear Optics. 1994, pp.130-136). The package also includes data preparation tools which can also be used separately for various applications.