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.
Version: 0.6.1 Depends: R (≥ 3.6.0), survival (≥ 2.44.1) Imports: coda (≥ 0.19.3), graphics (≥ 3.6.0), MASS (≥ 7.3.51), Matrix (≥ 1.2.17), RSpectra (≥ 0.16.0), sn (≥ 1.5.4), stats, utils (≥ 3.6.0) Suggests: knitr (≥ 1.26), rmarkdown (≥ 1.14), testthat (≥ 2.3.1) Published: 2022-08-20 DOI: 10.32614/CRAN.package.blapsr Author: Oswaldo Gressani [aut, cre] (Author), Philippe Lambert [aut, ths] (Co-author and thesis advisor) Maintainer: Oswaldo Gressani <oswaldo_gressani at hotmail.fr> License: GPL-3 Copyright: see file COPYRIGHTS URL: <https://www.blapsr-project.org/> NeedsCompilation: no Citation: blapsr citation info Materials: README NEWS CRAN checks: blapsr results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=blapsr to link to this page.
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