vagam-package: Variational approximations for generalized additive models

In vagam: Variational Approximations for Generalized Additive Models

Description

Fits generalized additive models (GAMs) using a variational approximations (VA) framework. In brief, the VA framework provides a fully or at least closed to fully tractable lower bound approximation to the marginal likelihood of a GAM when it is parameterized as a mixed model (using penalized splines, say). In doing so, the VA framework aims offers both the stability and natural inference tools available in the mixed model approach to GAMs, while achieving computation times comparable to that of using the penalized likelihood approach to GAMs. See Hui et al. (2018) <doi:10.1080/01621459.2018.1518235>.

Author(s)

Han Lin Shang [aut, cre, cph] (<https://orcid.org/0000-0003-1769-6430>), Francis K.C. Hui [aut] (<https://orcid.org/0000-0003-0765-3533>) Maintainer: Han Lin Shang <[e-mail]>

References

• Hui, F. K. C., You, C., Shang, H. L., and Mueller, S. (2018). Semiparametric regression using variational approximations, Journal of the American Statistical Association, forthcoming.

Examples

## Please see examples in the help file for the vagam function.

Example output

Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.
Loading required package: gamm4
Loading required package: Matrix
Loading required package: lme4

Attaching package: ‘lme4’

The following object is masked from ‘package:nlme’:

    lmList

This is gamm4 0.2-6

Loading required package: mvtnorm
Loading required package: truncnorm

vagam documentation built on Dec. 7, 2019, 1:06 a.m.