Bayesian Regression with Time-Varying Coefficients

Bayesian dynamic regression models where the regression coefficients can vary over time as random walks. Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo computations are done using Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for efficient sampling.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.2.0 by Jouni Helske, 2 months ago

Browse source code at

Authors: Jouni Helske

Documentation:   PDF Manual  

GPL (>= 2) license

Imports dplyr, ggplot2, KFAS, methods

Depends on Rcpp, bayesplot, rstan

Suggests diagis, gridExtra, knitr, rmarkdown, testthat

Linking to StanHeaders, rstan, BH, Rcpp, RcppArmadillo, RcppEigen

System requirements: C++11

See at CRAN