Enhanced Implementation of Whittaker-Henderson Smoothing
Diagonal Stacking of Matrices
Build Difference Matrix of Given Order
Deviance Residuals for Poisson GLM
Eigen Decomposition of Penalization Matrix
Diagnosis for Model Fit
Lapply with Custom Return Type
Bivariate Lapply with Custom Return Type
Provide WH model Fit Results as a Data.frame
Plot Method for a Whittaker-Henderson Fit
Plot Method for a Whittaker-Henderson Fit
Prediction for a Whittaker-Henderson Fit
Prediction for a Whittaker-Henderson Fit
Print Method for a Whittaker-Henderson Fit
Print Method for a Whittaker-Henderson Fit
Whittaker-Henderson Smoothing (Maximum Likelihood, fixed lambda)
Whittaker-Henderson Smoothing (Maximum Likelihood, optimize function)
Whittaker-Henderson Smoothing (Maximum Likelihood, Generalized Fellner...
1D Whittaker-Henderson Smoothing
2D Whittaker-Henderson Smoothing (Maximum Likelihood, fixed lambda)
2D Whittaker-Henderson Smoothing (Maximum Likelihood, optim function)
2D Whittaker-Henderson Smoothing (Maximum Likelihood, Generalized Fell...
2D Whittaker-Henderson Smoothing
WH : Enhanced Implementation of Whittaker-Henderson Smoothing
An enhanced implementation of Whittaker-Henderson smoothing for the gradation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2023) <doi:10.48550/arXiv.2306.06932>. Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.