A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.

Bayesian dynamic borrowing is an approach to incorporating external
   data to supplement a randomized, controlled trial analysis in which
   external data are incorporated in a dynamic way (e.g., based on similarity
   of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview.
   This package implements the hierarchical commensurate prior approach to dynamic borrowing
   as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>.
   There are three main functionalities. First, 'psborrow2' provides a user-friendly
   interface for applying dynamic borrowing on the study results handles the Markov Chain
   Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a
   simulation framework to compare different borrowing parameters (e.g. full borrowing, no
   borrowing, dynamic borrowing) and other trial and borrowing characteristics
   (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides
   a set of functions to generate data for simulation studies, and also allows
   the user to specify their own data generation process. This package is designed to
   use the sampling functions from 'cmdstanr' which can be installed from
   <https://stan-dev.r-universe.dev>.

Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome.  Both tumor-normal paired and tumor-only analyses are supported.

Bayesian analysis of item-response theory (IRT) models,
  roll call analysis; computing highest density regions; 
  maximum likelihood estimation of zero-inflated and hurdle models for count data;
  goodness-of-fit measures for GLMs;
  data sets used in writing	and teaching; seats-votes curves.

Provides a number of functions to
  simplify and automate the scoring, comparison, and evaluation of
  different ways of creating composites of data.  It is particularly
  aimed at facilitating the creation of physiological composites of
  metabolic syndrome symptom score (MetSSS) and allostatic load (AL).
  Provides a wrapper to calculate the MetSSS on new data using the
  Healthy Hearts formula.