Bayesian Inference with Laplace Approximations and P-Splines
Test positive definiteness and adjust positive definite matrix.
Object resulting from the fit of an additive partial linear model.
Bayesian additive partial linear modeling with Laplace-P-splines.
Extract estimated baseline quantities from a fit with coxlps.
Object from a Cox proportional hazards fit with Laplace-P-splines.
Fit a Cox proportional hazards regression model with Laplace-P-splines...
Construct a cubic B-spline basis.
Extract estimates of survival functions and cure probability for the p...
Object from a promotion time model fit with Laplace-P-splines.
Promotion time cure model with Laplace P-splines.
Object resulting from the fit of a generalized additive model.
Bayesian generalized additive modeling with Laplace-P-splines.
Specification of covariates entering the long-term part in a promotion...
Plot the approximate posterior distribution of the penalty vector.
Plot smooth functions of an additive model object.
Plot baseline hazard and survival curves from a coxlps object.
Plot estimated survival functions and cure probability for the promoti...
Plot smooth functions of a generalized additive model object.
Print an additive partial linear model object.
Print a coxlps object.
Print the fit of a promotion time cure model.
Print a generalized additive model object.
Simulation of survival times for the promotion time cure model.
Simulation of data for (Generalized) additive models.
Simulation of right censored survival times for the Cox model.
Specification of smooth terms in (g)amlps function.
Fit a skew-normal distribution to a target density.
Specification of covariates entering the short-term part in a promotio...
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.