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 Lambert
Show author detailsRolesAuthor, Thesis advisor
Insights
Last 30 days
This package has been downloaded 234 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2 times.
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Last 365 days
This package has been downloaded 2,943 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Aug 22, 2024 with 34 downloads.
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Dependencies
- Depends1 package
- Imports5 packages
- Suggests3 packages