Contains human behaviour datasets collected by the SAMPLING project (<https://sampling.warwick.ac.uk>).

Implementation of the SAM prior and generation of its
     operating characteristics for dynamically borrowing information
     from historical data. For details, please refer to Yang et al. (2023)
     <doi:10.1111/biom.13927>.

Estimation of the required sample size to validate a risk model for binary outcomes, based on the sample size equations proposed by Pavlou et al. (2021) <doi:10.1177/09622802211007522>. For precision-based sample size calculations, the user is required to enter the anticipated values of the C-statistic and outcome prevalence, which can be obtained from a previous study. The user also needs to specify the required precision (standard error) for the C-statistic, the calibration slope and the calibration in the large. The calculations are valid under the assumption of marginal normality for the distribution of the linear predictor.

Significance Analysis of Microarrays for differential expression analysis, RNAseq data and related problems.

Markov chain Monte Carlo samplers for posterior simulations of conjugate Bayesian nonparametric
    mixture models. Functionality is provided for Gibbs sampling as in Algorithm 3 of Neal (2000)
    <DOI:10.1080/10618600.2000.10474879>, restricted Gibbs merge-split sampling as described in Jain & Neal
    (2004) <DOI:10.1198/1061860043001>, and sequentially-allocated merge-split sampling <DOI:10.1080/00949655.2021.1998502>, as well as
    summary and utility functions.

Simulation tools for closed-loop simulation are provided for the 'MSEtool' operating model to inform data-rich fisheries. 
  'SAMtool' provides a conditioning model, assessment models of varying complexity with standardized reporting, 
  model-based management procedures, and diagnostic tools for evaluating assessments inside closed-loop simulation.

A sensitivity analysis approach for unmeasured confounding in observational data with multiple treatments and a binary outcome. This approach derives the general bias formula and provides adjusted causal effect estimates in response to various assumptions about the degree of unmeasured confounding. Nested multiple imputation is embedded within the Bayesian framework to integrate   uncertainty about the sensitivity parameters and sampling variability.  Bayesian Additive Regression Model (BART) is used for outcome modeling. The causal estimands are the conditional average treatment effects (CATE) based on the risk difference.  For more details, see paper: Hu L et al. (2020) A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data <arXiv:2012.06093>.