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:Samuel Thomas [cre, aut], Wanzhu Tu [ctb], Trustees of Columbia University [cph]

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'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • 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.

1.70 score 2 scripts 271 downloads 47 exports 71 dependencies

Last updated 3 years agofrom:545338e10e. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-linux-x86_64NOTEOct 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 pageTopics
The 'bayesGAM' package.bayesGAM-package
'bayesGAM' fits a variety of regression models using Hamiltonian Monte CarlobayesGAM
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 studybloodpressure
Extract Model Coefficientscoef,bayesGAMfit-method coefficients coefficients,bayesGAMfit-method
Creates a design matrix from a bivariate smoothing algorithmcreate_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' objectgetDesign 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 modelsL
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' packagemcmc_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' objectsmvcorrplot mvcorrplot,bayesGAMfit-method
Constructor function for Normal priorsnormal
Creates design matrices for univariate and bivariate applicationsnp
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' packageppc_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 datapredict predict,bayesGAMfit-method
Coral reef data from survey data on 6 sitesreef
Display the priors used in 'bayesGAM'showPrior showPrior,bayesGAMfit-method
Constructor function for Student-t priorsst
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