walker: Bayesian Generalized Linear Models with Time-Varying Coefficients
Walker provides a method for fully Bayesian generalized linear regression where the regression coefficients are allowed to vary over time as a first or second order integrated random walk.
The Markov chain Monte Carlo (MCMC) algorithm uses Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for accurate and efficient sampling. For non-Gaussian models the MCMC targets approximate marginal posterior based on Gaussian approximation, which is then corrected using importance sampling as in Vihola, Helske, Franks (2018).
See the package vignette and documentation manual for details and examples.
NEWS
12.5.2020
- Added as.data.frame function for
walker and walker_glm output.
- Added a
summary method.
- The print method now correctly warns about approximate results in case of non-Gaussian model.
- Changed arguments
*_prior to more concise versions (e.g. sigma_prior is now just sigma).
- Changed the name of the slope terms to
nu as in vignette formulas.
- Updated to rstantools 2.0.0 package structure and removed dependency on soft-depracated functions of
dplyr.
23.1.2020
- Removed check for missing values in function
walker which threw an error even though missing values in responses have been in principle supported since 2018…
20.9.2019
- Switched from GPL2+ to GPL3 in order to be compatible with future Stan versions.
04.03.2019
- Added methods fitted and coef for extracting the posterior means and and regression coefficents from the walker_fit object.
- Fixed issue with Makevars and clang4 per request by CRAN.
- Added option to predict on mean-scale, e.g, probabilities instead of 0/1 in Bernoulli case.
- Fixed a bug in the Gaussian predictions, last time point was missing the observational level noise.
25.02.2019
- Issue with upcoming staged installation in CRAN fixed by Tomas Kalibera.
14.02.2019
- Dimension bug in GLM case fixed.
8.11.2018
- Fixed StanHeaders search in Makevars.
22.10.2018
- Pull request by Ben Goodrich for fixing the issue with clang4. New version on it’s way to CRAN.
15.10.2018
- Missing values in response variable are now supported.
- Added gamma variables to models which can be used to damp the variance of the random walks.
- Tidied some Stan codes in order to reduce deep copying.
- Moved stan codes under
src.
- Increased the iteration counts in examples in order to pass CRAN tests. <