Bayesian Distributed Lag Model Fitting for Binary and Count Response Data
The Asymmetric Laplacian Distribution
Creating a DLM-Ready Dataframe
Fitting Discrete Distributed Lag Models with Variable Selection via MC...
Negative Binomial Regression via MCMC
Visualises Results from MCMC_DLM Fits
Binary Quantile Regression via MCMC
Tools for fitting Bayesian Distributed Lag Models (DLMs) to longitudinal response data that is a count or binary. Count data is fit using negative binomial regression and binary is fit using quantile regression. The contribution of the lags are fit via b-splines. In addition, infers the predictor inclusion uncertainty. Multimomial models are not supported. Based on Dempsey and Wyse (2025) <doi:10.48550/arXiv.2403.03646>.
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