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>.

Augmenting a matched data set by generating multiple stochastic, matched samples from the data using a
  multi-dimensional histogram constructed from dropping the input matched data into a multi-dimensional grid built on
  the full data set. The resulting stochastic, matched sets will likely provide a collectively higher coverage of the full
  data set compared to the single matched set. Each stochastic match is without duplication, thus allowing downstream
  validation techniques such as cross-validation to be applied to each set without concern for overfitting.

An efficient tool for fitting nested mixture models based on a shared set of 
    atoms via Markov Chain Monte Carlo and variational inference algorithms. 
    Specifically, the package implements the common atoms model (Denti et al., 2023), 
    its finite version (similar to D'Angelo et al., 2023), and a hybrid finite-infinite 
    model (D'Angelo and Denti, 2024). All models implement univariate nested mixtures 
    with Gaussian kernels equipped with a normal-inverse gamma prior distribution 
    on the parameters. Additional functions are provided to help analyze the 
    results of the fitting procedure.   
    References:       
    Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>,      
    D’Angelo, Canale, Yu, Guindani (2023) <doi:10.1111/biom.13626>,      
    D’Angelo, Denti (2024) <doi:10.1214/24-BA1458>.