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Bayesian Marginal Effects for 'brms' Models
Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the 'brms' package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example
Adjust Longitudinal Regression Models Using Bayesian Methodology
Adjusts longitudinal regression models using Bayesian methodology for covariance structures of composite symmetry (SC), autoregressive ones of order 1 AR (1) and autoregressive moving average of order (1,1) ARMA (1,1).
High-Dimensional Model Selection
Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025)
Combining Subset MCMC Samples to Estimate a Posterior Density
See Miroshnikov and Conlon (2014)
Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
Bayesian Estimation of ARIMAX Model
The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX by Bierens (1987)
Bayesian Additive Regression Trees for Confounder Selection
Fit Bayesian Regression Additive Trees (BART) models to
select true confounders from a large set of potential confounders and
to estimate average treatment effect. For more information, see Kim et
al. (2023)
Bayesian Optimal INterval (BOIN) Design for Single-Agent and Drug- Combination Phase I Clinical Trials
The Bayesian optimal interval (BOIN) design is a novel phase I
clinical trial design for finding the maximum tolerated dose (MTD). It can be
used to design both single-agent and drug-combination trials. The BOIN design
is motivated by the top priority and concern of clinicians when testing a new
drug, which is to effectively treat patients and minimize the chance of exposing
them to subtherapeutic or overly toxic doses. The prominent advantage of the
BOIN design is that it achieves simplicity and superior performance at the same
time. The BOIN design is algorithm-based and can be implemented in a simple
way similar to the traditional 3+3 design. The BOIN design yields an average
performance that is comparable to that of the continual reassessment method
(CRM, one of the best model-based designs) in terms of selecting the MTD, but
has a substantially lower risk of assigning patients to subtherapeutic or overly
toxic doses. For tutorial, please check Yan et al. (2020)
Tidy Methods for Bayesian Treatment Effect Models
Functions for extracting tidy data from Bayesian treatment effect models, in particular BART, but extensions are possible. Functionality includes extracting tidy posterior summaries as in 'tidybayes' < https://github.com/mjskay/tidybayes>, estimating (average) treatment effects, common support calculations, and plotting useful summaries of these.
Plotting for Bayesian Models
Plotting functions for posterior analysis, MCMC diagnostics,
prior and posterior predictive checks, and other visualizations
to support the applied Bayesian workflow advocated in
Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019)