Package: bayesGAM 0.0.2
Samuel Thomas
bayesGAM: Fit Multivariate Response Generalized Additive Models using Hamiltonian Monte Carlo
The 'bayesGAM' package is designed to provide a user friendly option to fit univariate and multivariate response Generalized Additive Models (GAM) using Hamiltonian Monte Carlo (HMC) with few technical burdens. The functions in this package use 'rstan' (Stan Development Team 2020) to call 'Stan' routines that run the HMC simulations. The 'Stan' code for these models is already pre-compiled for the user. The programming formulation for models in 'bayesGAM' is designed to be familiar to analysts who fit statistical models in 'R'. Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of statistical software, 76(1). Stan Development Team. 2018. RStan: the R interface to Stan. R package version 2.17.3. <https://mc-stan.org/> Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418. Betancourt, Michael, and Mark Girolami. "Hamiltonian Monte Carlo for hierarchical models." Current trends in Bayesian methodology with applications 79.30 (2015): 2-4. Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arxiv:2006.16194>, Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174. Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric regression (No. 12). Cambridge university press. ISBN: 978-0521785167.
Authors:
bayesGAM_0.0.2.tar.gz
bayesGAM_0.0.2.tar.gz(r-4.5-noble)bayesGAM_0.0.2.tar.gz(r-4.4-noble)
bayesGAM.pdf |bayesGAM.html✨
bayesGAM/json (API)
NEWS
# Install 'bayesGAM' in R: |
install.packages('bayesGAM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- bloodpressure - Blood pressure data from a clinical study
- reef - Coral reef data from survey data on 6 sites
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:545338e10e. Checks:OK: 1 NOTE: 1. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 26 2024 |
R-4.5-linux-x86_64 | NOTE | Oct 26 2024 |
Exports:bayesGAMcoefcoefficientscreate_bivariate_designextract_log_lik_bgamfittedgetDesigngetModelSlotsgetSamplesgetStanResultsLloo_bgamloo_compare_bgammcmc_acfmcmc_acf_barmcmc_areasmcmc_densmcmc_hexmcmc_histmcmc_hist_by_chainmcmc_intervalsmcmc_neffmcmc_neff_datamcmc_neff_histmcmc_pairsmcmc_rhatmcmc_rhat_datamcmc_rhat_histmcmc_scattermcmc_tracemcmc_violinmvcorrplotnormalnpplotposterior_predictppc_boxplotppc_densppc_dens_overlayppc_ecdf_overlayppc_freqpolyppc_histpredictshowPriorstsummarywaic_bgam
Dependencies:abindbackportsbayesplotBHbootcallrcheckmatecliclustercolorspacecorrplotdescdistributionaldplyrfansifarvergenericsgeometryggplot2ggridgesgluegridExtragtableinlineisobandlabelinglatticelifecyclelinprogloolpSolvemagicmagrittrMASSMatrixmatrixStatsmgcvmlbenchmunsellnlmenumDerivpillarpkgbuildpkgconfigplyrposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelRcppProgressreshape2rlangrstanrstantoolsscalesSemiParStanHeadersstringistringrtensorAtibbletidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
The 'bayesGAM' package. | bayesGAM-package |
'bayesGAM' fits a variety of regression models using Hamiltonian Monte Carlo | bayesGAM |
Contains results from 'rstan' as well as the design matrices and other data for the model. | bayesGAMfit bayesGAMfit-class show,bayesGAMfit-method |
Blood pressure data from a clinical study | bloodpressure |
Extract Model Coefficients | coef,bayesGAMfit-method coefficients coefficients,bayesGAMfit-method |
Creates a design matrix from a bivariate smoothing algorithm | create_bivariate_design |
Extract the log likelihood from models fit by 'bayesGAM' | extract_log_lik_bgam extract_log_lik_bgam,bayesGAMfit-method |
Extract fitted values from a model fit by 'bayesGAM' | fitted fitted,bayesGAMfit-method |
Design matrices from a 'bayesGAMfit' object | getDesign getDesign,bayesGAMfit-method getDesign,glmModel-method |
Return one or slots from the 'Stan' model in 'bayesGAM' | getModelSlots getModelSlots,bayesGAMfit-method |
Extract the MCMC samples from an object of type 'bayesGAMfit' | getSamples getSamples,bayesGAMfit-method getSamples,glmModel-method getSamples,stanfit-method |
Returns the 'stanfit' object generated by 'rstan' | getStanResults getStanResults,bayesGAMfit-method |
Lag function for autoregressive models | L |
Calls the 'loo' package to perform efficient approximate leave-one-out cross-validation on models fit with 'bayesGAM' | loo_bgam loo_bgam,array-method loo_bgam,bayesGAMfit-method |
Calls the 'loo' package to compare models fit by 'bayesGAMfit' | loo_compare_bgam loo_compare_bgam,bayesGAMfit-method |
Plotting for MCMC visualization and diagnostics provided by 'bayesplot' package | mcmc_acf mcmc_acf,bayesGAMfit-method mcmc_acf_bar mcmc_acf_bar,bayesGAMfit-method mcmc_areas mcmc_areas,bayesGAMfit-method mcmc_dens mcmc_dens,bayesGAMfit-method mcmc_hex mcmc_hex,bayesGAMfit-method mcmc_hist mcmc_hist,bayesGAMfit-method mcmc_hist_by_chain mcmc_hist_by_chain,bayesGAMfit-method mcmc_intervals mcmc_intervals,bayesGAMfit-method mcmc_neff mcmc_neff,bayesGAMfit-method mcmc_neff_data mcmc_neff_data,bayesGAMfit-method mcmc_neff_hist mcmc_neff_hist,bayesGAMfit-method mcmc_pairs mcmc_pairs,bayesGAMfit-method mcmc_plots mcmc_rhat mcmc_rhat,bayesGAMfit-method mcmc_rhat_data mcmc_rhat_data,bayesGAMfit-method mcmc_rhat_hist mcmc_rhat_hist,bayesGAMfit-method mcmc_scatter mcmc_scatter,bayesGAMfit-method mcmc_trace mcmc_trace,bayesGAMfit-method mcmc_violin mcmc_violin,bayesGAMfit-method |
Multivariate response correlation plot for 'bayesGAMfit' objects | mvcorrplot mvcorrplot,bayesGAMfit-method |
Constructor function for Normal priors | normal |
Creates design matrices for univariate and bivariate applications | np |
Additional plotting for MCMC visualization and diagnostics. | plot plot,bayesGAMfit,missing-method plot,posteriorPredictObject,missing-method plot,predictPlotObject,missing-method |
Posterior predictive samples from models fit by 'bayesGAM' | posterior_predict posterior_predict,bayesGAMfit-method posterior_predict,glmModel-method |
Plotting for MCMC visualization and diagnostics provided by 'bayesplot' package | ppc_boxplot ppc_boxplot,bayesGAMfit-method ppc_boxplot,posteriorPredictObject-method ppc_dens ppc_dens,bayesGAMfit-method ppc_dens,posteriorPredictObject-method ppc_dens_overlay ppc_dens_overlay,bayesGAMfit-method ppc_dens_overlay,posteriorPredictObject-method ppc_ecdf_overlay ppc_ecdf_overlay,bayesGAMfit-method ppc_ecdf_overlay,posteriorPredictObject-method ppc_freqpoly ppc_freqpoly,bayesGAMfit-method ppc_freqpoly,posteriorPredictObject-method ppc_hist ppc_hist,bayesGAMfit-method ppc_hist,posteriorPredictObject-method ppc_plots |
Posterior predictive samples from models fit by 'bayesGAM', but with new data | predict predict,bayesGAMfit-method |
Coral reef data from survey data on 6 sites | reef |
Display the priors used in 'bayesGAM' | showPrior showPrior,bayesGAMfit-method |
Constructor function for Student-t priors | st |
Summarizing Model Fits from 'bayesGAM' | summary summary,bayesGAMfit-method |
Calls the 'loo' package to calculate the widely applicable information criterion (WAIC) | waic_bgam waic_bgam,array-method waic_bgam,bayesGAMfit-method |