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Approximate Bayesian inference with blapsr

 

 

 

Purpose

The blapsr package consists in a set of routines that can be used for analysis in survival models and (generalized) additive models. The methodology is based on the combination of Bayesian P-splines for flexible estimation of smooth functions and Laplace approximations to (selected) posterior distributions.

Version

Website

A website is dedicated to the package: https://www.blapsr-project.org. The link to the CRAN page is https://cran.r-project.org/package=blapsr .

Special thanks

This package was developed during a PhD thesis at Université catholique de Louvain (Belgium) that was funded in part by the Actions de Recherche Concertées (ARC 11/16-039) grant and another grant obtained from the Luxembourgish Ministry of higher education.

We are grateful to Vincent Bremhorst for providing the function on which the simcuredata.R routine is based. The latter is used for generating survival times in the promotion time cure model (Bremhorst and Lambert, 2016).

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Version

Install

install.packages('blapsr')

Monthly Downloads

558

Version

0.6.1

License

GPL-3

Maintainer

Oswaldo Gressani

Last Published

August 20th, 2022

Functions in blapsr (0.6.1)

curelps.extract

Extract estimates of survival functions and cure probability for the promotion time cure model.
adjustPD

Test positive definiteness and adjust positive definite matrix.
amlps

Bayesian additive partial linear modeling with Laplace-P-splines.
amlps.object

Object resulting from the fit of an additive partial linear model.
cubicbs

Construct a cubic B-spline basis.
curelps

Promotion time cure model with Laplace P-splines.
curelps.object

Object from a promotion time model fit with Laplace-P-splines.
coxlps

Fit a Cox proportional hazards regression model with Laplace-P-splines.
coxlps.baseline

Extract estimated baseline quantities from a fit with coxlps.
coxlps.object

Object from a Cox proportional hazards fit with Laplace-P-splines.
plot.coxlps

Plot baseline hazard and survival curves from a coxlps object.
ecog1684

Phase III Melanoma clinical trial.
plot.curelps

Plot estimated survival functions and cure probability for the promotion time cure model.
penaltyplot

Plot the approximate posterior distribution of the penalty vector.
lt

Specification of covariates entering the long-term part in a promotion time cure model.
laryngeal

Survival data of male laryngeal cancer patients.
plot.amlps

Plot smooth functions of an additive model object.
print.coxlps

Print a coxlps object.
print.curelps

Print the fit of a promotion time cure model.
gamlps

Bayesian generalized additive modeling with Laplace-P-splines.
medicaid

Data from the 1986 Medicaid Consumer Survey.
plot.gamlps

Plot smooth functions of a generalized additive model object.
print.amlps

Print an additive partial linear model object.
melanoma

Melanoma survival data.
sm

Specification of smooth terms in (g)amlps function.
gamlps.object

Object resulting from the fit of a generalized additive model.
simgamdata

Simulation of data for (Generalized) additive models.
kidneytran

Survival data of kidney transplant patients.
simsurvdata

Simulation of right censored survival times for the Cox model.
st

Specification of covariates entering the short-term part in a promotion time cure model.
simcuredata

Simulation of survival times for the promotion time cure model.
print.gamlps

Print a generalized additive model object.
snmatch

Fit a skew-normal distribution to a target density.