walker:
Bayesian Generalized Linear Models with Time-Varying Coefficients
The R package 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 (2020).
See the corresponding paper in softwareX
for short introduction, and the package vignette
and documentation
manual for details and further examples.
You can download the development version of walker from
Github using the devtools
package:
devtools::install_github("helske/walker")
NEWS
11.9.2023, version 1.0.8
- Updated the Stan codes to accomodate the new array syntax. PR by
Andrew Johnson.
25.7.2023, version 1.0.7
- Fixed the initial values in the examples of walker in order to get
sampler started.
- Fully delegated the installation to rstantools via PR by Andrew
Johnson.
13.10.2022, version 1.0.6
- Fixed the LFO computations in case the data contains missing
values.
10.7.2022, version 1.0.5
- Improved documentation of the gamma variables in
walker, rw1 and rw2.
3.3.2022, version 1.0.4
- Added an example of counterfactual predictions to the vignette.
- Added citation info for the softwareX paper.
24.9.2021, version 1.0.3-1
- Added a flag for stanc3 compatibility, thanks to Andrew Johnson.
Also added an import for RcppParallel.
16.8.2021
- Internal changes to make
walker compatible with
upcoming StanHeaders.
6.4.2021
- Changed the name of the
logLik variable to
log_lik so it is compatible with loo.
27.1.2021
- Fixed some issues in the vignette which resulted CRAN warnings.
25.1.2021
- For linear-Gaussian models the stanfit object now returns partial
log-likelihood terms p(y_t | y_1,…,y_t-1,theta) which can be used for
leave-future-out cross-validation (see function
lfo).
- New function
lfo for estimating the leave-future-out
information criterion.
- Priors for the standard deviation parameters are now Gamma instead
of truncated normal, which helps to avoid (rare) problems where sampler
wonders close to degenerate case of having all variances near zero.
There are also default prior Gamma(2, 0.0001) for these parameters
now.
- Fixed some issues in the vignette added a reference to the walker
paper.
3.11.2020
- stanfit object of walker output now contains also variable
logLik. For non-Gaussian models this is the approximate
log-likelihood, the unbiased estimate is then
logLik + mean(w), where w are the returned
weights.
19.10.2020
- Predict method now allow predictions on link scale.
- Added argument for plot_predict for controlling the drawing of past
observations.
- Fix out-of-sample predictions for non-Gaussian models.
- New function:
predict_counterfactual which can be used
to predict the past assuming new values for the covariates.
13.8.2020
- Proper export of
pp_check for bayesplot,
fixed some minor technical issues.
19.5.2020
- Added default values for
row.names and
optional for as.data.frame function.
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. <