Fit Multivariate Response Generalized Additive Models using Hamiltonian Monte Carlo
The 'bayesGAM' package.
bayesGAM fits a variety of regression models using Hamiltonian Monte...
Contains results from rstan as well as the design matrices and other...
Extract Model Coefficients
Creates a design matrix from a bivariate smoothing algorithm
Extract the log likelihood from models fit by bayesGAM
Extract fitted values from a model fit by bayesGAM
Design matrices from a bayesGAMfit object
Return one or slots from the Stan model in bayesGAM
Extract the MCMC samples from an object of type bayesGAMfit
Returns the stanfit object generated by rstan
Lag function for autoregressive models
Calls the loo package to perform efficient approximate leave-one-out...
Calls the loo package to compare models fit by bayesGAMfit
Plotting for MCMC visualization and diagnostics provided by `bayesplot...
Multivariate response correlation plot for bayesGAMfit objects
Constructor function for Normal priors
Creates design matrices for univariate and bivariate applications
Additional plotting for MCMC visualization and diagnostics.
Posterior predictive samples from models fit by bayesGAM
Plotting for MCMC visualization and diagnostics provided by `bayesplot...
Posterior predictive samples from models fit by bayesGAM, but with n...
Display the priors used in bayesGAM
Constructor function for Student-t priors
Summarizing Model Fits from bayesGAM
Calls the loo package to calculate the widely applicable information...
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.