Regression inference for multiple populations by integrating 
    summary-level data using stacked imputations. Gu, T., Taylor, J.M.G. and 
    Mukherjee, B. (2021) A synthetic data integration framework to leverage 
    external summary-level information from heterogeneous populations 
    <arXiv:2106.06835>.

A framework for evaluating drug combination effects in preclinical in vivo studies.
    'SynergyLMM' provides functions to analyze longitudinal tumor growth experiments using mixed-effects models, 
    perform time-resolved analyses of synergy and antagonism, evaluate model diagnostics and performance, 
    and assess both post-hoc and a priori statistical power.
    The calculation of drug combination synergy follows the statistical framework provided by Demidenko and Miller (2019, <doi:10.1371/journal.pone.0224137>).
    The implementation and analysis of linear mixed-effect models is based on the methods described by Pinheiro and Bates (2000, <doi:10.1007/b98882>),
    and Gałecki and Burzykowski (2013, <doi:10.1007/978-1-4614-3900-4>).

This tool fits a non-parametric Bayesian model called a "hierarchically coupled mixture model with local dependence (HCMM-LD)" to the original microdata in order to generate synthetic microdata for privacy protection. The non-parametric feature of the adopted model is useful for capturing the joint distribution of the original input data in a highly flexible manner, leading to the generation of synthetic data whose distributional features are similar to that of the input data. The package allows the original input data to have missing values and impute them with the posterior predictive distribution, so no missing values exist in the synthetic data output. The method builds on the work of Murray and Reiter (2016) <doi:10.1080/01621459.2016.1174132>.

Explore synesthesia
  consistency test data, calculate consistency scores, 
  and classify participant data as valid or invalid.

A set of functions for generating 'SPSS' syntax files from the R environment.