Package: walker 1.0.10
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
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walker_1.0.10.tgz(r-4.5-x86_64)walker_1.0.10.tgz(r-4.5-arm64)walker_1.0.10.tgz(r-4.4-x86_64)walker_1.0.10.tgz(r-4.4-arm64)walker_1.0.10.tgz(r-4.3-x86_64)walker_1.0.10.tgz(r-4.3-arm64)
walker_1.0.10.tar.gz(r-4.5-noble)walker_1.0.10.tar.gz(r-4.4-noble)
walker.pdf |walker.html✨
walker/json (API)
| # Install 'walker' in R: |
| install.packages('walker', repos = c('https://helske.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/helske/walker/issues
bayesiangeneralized-linear-modelsmcmcstantime-seriesopenblascpp
Last updated 5 months agofrom:1d02788842. Checks:4 OK, 7 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 26 2025 |
| R-4.5-win-x86_64 | NOTE | Jan 26 2025 |
| R-4.5-mac-x86_64 | OK | Jan 26 2025 |
| R-4.5-mac-aarch64 | OK | Jan 26 2025 |
| R-4.5-linux-x86_64 | OK | Jan 26 2025 |
| R-4.4-win-x86_64 | NOTE | Jan 26 2025 |
| R-4.4-mac-x86_64 | NOTE | Jan 26 2025 |
| R-4.4-mac-aarch64 | NOTE | Dec 27 2024 |
| R-4.3-win-x86_64 | NOTE | Jan 26 2025 |
| R-4.3-mac-x86_64 | NOTE | Jan 26 2025 |
| R-4.3-mac-aarch64 | NOTE | Dec 27 2024 |
Exports:lfoplot_coefsplot_fitplot_predictpredict_counterfactualrw1rw2walkerwalker_glmwalker_rw1
Dependencies:abindbackportsbase64encbayesplotBHbslibcachemcallrcheckmatecliclustercodacolorspacedata.tabledescdigestdistributionaldplyrevaluatefansifarverfastmapfontawesomeforeignFormulafsgenericsggplot2ggridgesgluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsinlineisobandjquerylibjsonliteKFASknitrlabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmemoisemgcvmimemunsellnlmennetnumDerivpillarpkgbuildpkgconfigplyrposteriorprocessxpsQuickJSRR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelreshape2rlangrmarkdownrpartrstanrstantoolsrstudioapisassscalesStanHeadersstringistringrtensorAtibbletidyselecttinytexutf8vctrsviridisviridisLitewithrxfunyaml
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 |
