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.

Version: 0.5.5
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: 2020-10-19
Author: Oswaldo Gressani [aut, cre] (Author), Philippe Lambert [aut, ths] (Co-author and thesis advisor)
Maintainer: Oswaldo Gressani <oswaldo_gressani at>
License: GPL-3
Copyright: see file COPYRIGHTS
URL: <>
NeedsCompilation: no
Citation: blapsr citation info
Materials: README NEWS
CRAN checks: blapsr results


Reference manual: blapsr.pdf
Vignettes: blapsr for approximate Bayesian inference
Package source: blapsr_0.5.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: blapsr_0.5.5.tgz, r-oldrel: blapsr_0.5.5.tgz
Old sources: blapsr archive


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