Package: walker 1.0.10
Jouni Helske
walker: Bayesian Generalized Linear Models with Time-Varying Coefficients
Efficient Bayesian generalized linear models with time-varying coefficients as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo (MCMC) 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. For non-Gaussian models, the package uses the importance sampling type estimators based on approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).
Authors:
walker_1.0.10.tar.gz
walker_1.0.10.tar.gz(r-4.7-arm64)walker_1.0.10.tar.gz(r-4.7-x86_64)walker_1.0.10.tar.gz(r-4.6-arm64)walker_1.0.10.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
walker/json (API)
| # Install 'walker' in R: |
| install.packages('walker', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/helske/walker/issues
Last updated from:c20e51f95c. Checks:5 OK, 1 FAIL. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 464 | ||
| linux-devel-x86_64 | OK | 430 | ||
| source / vignettes | OK | 653 | ||
| linux-release-arm64 | OK | 456 | ||
| linux-release-x86_64 | OK | 474 | ||
| wasm-release | FAIL | 178 |
Exports:lfoplot_coefsplot_fitplot_predictpredict_counterfactualrw1rw2walkerwalker_glmwalker_rw1
Dependencies:abindbackportsbase64encbayesplotBHbslibcachemcallrcheckmatecliclustercodacolorspacecpp11data.tabledescdigestdistributionaldplyrevaluatefarverfastmapfontawesomeforeignFormulafsgenericsggplot2ggridgesgluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsinlineisobandjquerylibjsonliteKFASknitrlabelinglatticelifecycleloomagrittrmatrixStatsmemoisemimennetnumDerivpillarpkgbuildpkgconfigplyrposteriorprocessxpspurrrQuickJSRR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelreshape2rlangrmarkdownrpartrstanrstantoolsrstudioapiS7sassscalesStanHeadersstringistringrtensorAtibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Coerce Posterior Samples of walker Fit to a Data Frame | as.data.frame.walker_fit |
| Extract Coeffients of Walker Fit | coef.walker_fit |
| Extract Fitted Values of Walker Fit | fitted.walker_fit |
| Leave-Future-Out Cross-Validation | lfo |
| Posterior predictive check for walker object | plot_coefs |
| Plot the fitted values and sample quantiles for a walker object | plot_fit |
| Prediction intervals for walker object | plot_predict |
| Posterior predictive check for walker object | pp_check.walker_fit |
| Predictions for walker object | predict_counterfactual |
| Predictions for walker object | predict.walker_fit |
| Print Summary of walker_fit Object | print.walker_fit |
| Construct a first-order random walk component | rw1 |
| Construct a second-order random walk component | rw2 |
| Summary of walker_fit Object | summary.walker_fit |
| Bayesian regression with random walk coefficients | walker |
| Bayesian generalized linear model with time-varying coefficients | walker_glm |
| Comparison of naive and state space implementation of RW1 regression model | walker_rw1 |
