Package: blapsr 0.6.1

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.

Authors:Oswaldo Gressani [aut, cre], Philippe Lambert [aut, ths]

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blapsr.pdf |blapsr.html✨
blapsr/json (API)
NEWS

# Install 'blapsr' in R:
install.packages('blapsr', repos = c('https://oswaldogressani.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/oswaldogressani/blapsr/issues

Datasets:
  • ecog1684 - Phase III Melanoma clinical trial.
  • kidneytran - Survival data of kidney transplant patients.
  • laryngeal - Survival data of male laryngeal cancer patients.
  • medicaid - Data from the 1986 Medicaid Consumer Survey.
  • melanoma - Melanoma survival data.

On CRAN:

16 exports 5 stars 1.11 score 14 dependencies 4 scripts 277 downloads

Last updated 2 years agofrom:da0a6e735d. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 05 2024
R-4.5-winNOTESep 05 2024
R-4.5-linuxNOTESep 05 2024
R-4.4-winOKSep 05 2024
R-4.4-macOKSep 05 2024
R-4.3-winOKSep 05 2024
R-4.3-macOKSep 05 2024

Exports:adjustPDamlpscoxlpscoxlps.baselinecubicbscurelpscurelps.extractgamlpsltpenaltyplotsimcuredatasimgamdatasimsurvdatasmsnmatchst

Dependencies:codalatticeMASSMatrixMatrixModelsmnormtnumDerivquantregRcppRcppEigenRSpectrasnSparseMsurvival

blapsr for approximate Bayesian inference

Gressani Oswaldo

Rendered fromblapsr-vignette.Rmdusingknitr::rmarkdownon Sep 05 2024.

Last update: 2020-07-14
Started: 2020-07-14

Readme and manuals

Help Manual

Help pageTopics
Bayesian additive partial linear modeling with Laplace-P-splines.amlps
Object resulting from the fit of an additive partial linear model.amlps.object
Fit a Cox proportional hazards regression model with Laplace-P-splines.coxlps
Extract estimated baseline quantities from a fit with coxlps.coxlps.baseline
Object from a Cox proportional hazards fit with Laplace-P-splines.coxlps.object
Construct a cubic B-spline basis.cubicbs
Promotion time cure model with Laplace P-splines.curelps
Extract estimates of survival functions and cure probability for the promotion time cure model.curelps.extract
Object from a promotion time model fit with Laplace-P-splines.curelps.object
Phase III Melanoma clinical trial.ecog1684
Bayesian generalized additive modeling with Laplace-P-splines.gamlps
Object resulting from the fit of a generalized additive model.gamlps.object
Survival data of kidney transplant patients.kidneytran
Survival data of male laryngeal cancer patients.laryngeal
Data from the 1986 Medicaid Consumer Survey.medicaid
Melanoma survival data.melanoma
Plot the approximate posterior distribution of the penalty vector.penaltyplot
Plot smooth functions of an additive model object.plot.amlps
Plot baseline hazard and survival curves from a coxlps object.plot.coxlps
Plot estimated survival functions and cure probability for the promotion time cure model.plot.curelps
Plot smooth functions of a generalized additive model object.plot.gamlps
Print an additive partial linear model object.print.amlps
Print a coxlps object.print.coxlps
Print the fit of a promotion time cure model.print.curelps
Print a generalized additive model object.print.gamlps
Simulation of survival times for the promotion time cure model.simcuredata
Simulation of data for (Generalized) additive models.simgamdata
Simulation of right censored survival times for the Cox model.simsurvdata
Fit a skew-normal distribution to a target density.snmatch