blapsr
Bayesian Inference with Laplace Approximations and P-Splines
Laplace approximations and penalized B-splines are combined for fast Bayesian inference in latent Gaussian models. The routines can be used to fit survival models, especially proportional hazards and promotion time cure models (Gressani, O. and Lambert, P. (2018) doi:10.1016/j.csda.2018.02.007). The Laplace-P-spline methodology can also be implemented for inference in (generalized) additive models (Gressani, O. and Lambert, P. (2021) doi:10.1016/j.csda.2020.107088). See the associated website for more information and examples.
- Version0.6.1
- R version≥ 3.6.0
- LicenseGPL-3
- Needs compilation?No
- blapsr citation info
- Last release08/20/2022
Documentation
Team
- Oswaldo Gressani
- Philippe LambertShow author detailsRolesAuthor, Thesis advisor
Insights
Last 30 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends1 package
- Imports5 packages
- Suggests3 packages