The virtual g-prior class
The hyper-g prior class
The custom g-prior class
The incomplete inverse gamma g-prior class
Constructor for the hyper-g prior class
Extract method for GlmBayesMfp objects
Constructor for the custom g-prior class
Class for samples from a single GlmBayesMfp model or a model average
Initialization method for the "HypergPrior" class
Constructor for class McmcOptions
GlmBayesMfpSamples-subsetting
Subset method for GlmBayesMfpSamples objects
The inverse gamma g-prior class
Construct the covariates matrix for new data based on an existing GlmBayesMfp object
Convert samples to mcmc objects
Construct a (smooth) marginal z density approximation from a model
information list
as.data.frame.GlmBayesMfp
Convert a GlmBayesMfp object into a data frame
Constructor for the inverse gamma g-prior class
Interface to the internal C++ optimization routine "optimize"
InvGammaGPrior-initialize
Initialization method for the "InvGammaGPrior" class
Prediction methods for CoxTBF objects
IncInvGammaGPrior-initialize
Initialization method for the "IncInvGammaGPrior" class
Prediction methods for CoxTBF objects with separate estimates
Helper function for glmBayesMfp: Returns the normalized log g prior density
Box Tidwell transformation
Constructor for the incomplete inverse gamma g-prior class
Compute the Chib-Jeliazkov log marginal likelihood estimate from the MCMC output
Estimate shrunken coefficients from GlmBayesMfp object for Cox model
Construct a survival formula based on a glmBfp object with censInd not null.
Compute model information for a given list of model configurations and glmBayesMfp output.
Function for plotting a fractional polynomial curve estimate
Extract the log prior values from a GlmBayesMfp object
Construct an empirical HPD interval from samples
Class for the three canonical MCMC options
Evaluate the (negative log) unnormalized marginal z density in a given
model.
Fit Cox models using glmBayesMfp
Transform formula variables
Shift and scale a covariate vector (if wished) to have positive and small numbers.
Produce posterior samples from one GLM / Cox model
Prediction methods for CoxTBF objects for BMA models
Extract posterior model probability estimates from a GlmBayesMfp object
Interface to the internal C++ optimization routine "bfgs"
getUncenteredDesignMatrix
Construct the design matrix for a given bfp GLM model
Construct the design matrix for a given bfp GLM model
Bayesian model inference for fractional polynomial GLMs and Cox models
Mark a covariate for transformation with fractional polynomials
Predicate checking for a boolean option
Compute the number of samples for a given MCMC options triple
Bayesian inference for fractional polynomial models from the GLM
and Cox family
Helper function for glmBayesMfp: Extracts an S3 family object
Extract the log marginal likelihood estimates from a GlmBayesMfp object
Compute posterior inclusion probabilites based on GlmBayesMfp object
Internal helper function which gets the generator (and normalizing
constant)
Print a GlmBayesMfp object.
Test the Cox model computation for the TBF approach
Get the FP transforms matrix of a given covariate vector
Calculate an SCB from a samples matrix
Produce posterior samples from a Bayesian model average over GLMs / Cox
models