CRAN/E | blapsr

blapsr

Bayesian Inference with Laplace Approximations and P-Splines

Installation

About

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.

Citation blapsr citation info
<www.blapsr-project.org/>
Copyright see file COPYRIGHTS

Key Metrics

Version 0.6.1
R ≥ 3.6.0
Published 2022-08-20 615 days ago
Needs compilation? no
License GPL-3
CRAN checks blapsr results

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Maintainer

Maintainer

Oswaldo Gressani

oswaldo_gressani@hotmail.fr

Authors

Oswaldo Gressani

aut / cre

(Author)

Philippe Lambert

aut / ths

(Co-author and thesis advisor)

Material

README
NEWS
Reference manual
Package source

Vignettes

blapsr for approximate Bayesian inference

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

blapsr archive

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