Package: WH 2.0.0

WH: Enhanced Implementation of Whittaker-Henderson Smoothing

An enhanced implementation of Whittaker-Henderson smoothing for the graduation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2025) <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.

Authors:Guillaume Biessy [aut, cre, cph]

WH_2.0.0.tar.gz
WH_2.0.0.zip(r-4.7-x86_64)WH_2.0.0.zip(r-4.7-arm64)WH_2.0.0.zip(r-4.6-x86_64)WH_2.0.0.zip(r-4.6-arm64)WH_2.0.0.zip(r-4.5-x86_64)
WH_2.0.0.tgz(r-4.6-x86_64)WH_2.0.0.tgz(r-4.6-arm64)WH_2.0.0.tgz(r-4.5-x86_64)WH_2.0.0.tgz(r-4.5-arm64)
WH_2.0.0.tar.gz(r-4.7-arm64)WH_2.0.0.tar.gz(r-4.7-x86_64)WH_2.0.0.tar.gz(r-4.6-arm64)WH_2.0.0.tar.gz(r-4.6-x86_64)
WH_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
WH/json (API)

# Install 'WH' in R:

install.packages('WH', repos = c('https://guillaumebiessy.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/guillaumebiessy/wh/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

portfolio_LTC - Aggregated Long-Term Care Dataset
portfolio_mort - Aggregated Mortality Dataset

On CRAN:

actuarial-science bayesian-methods mortality-estimation smoothing-methods openblas cpp

5.34 score 11 stars 9 scripts 264 downloads 2 exports 1 dependencies

Last updated from:5564ea5add. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	134
linux-devel-x86_64	OK	142
source / vignettes	OK	210
linux-release-arm64	OK	125
linux-release-x86_64	OK	125
macos-release-arm64	OK	157
macos-release-x86_64	OK	240
macos-oldrel-arm64	OK	109
macos-oldrel-x86_64	OK	270
windows-devel-arm64	OK	219
windows-devel-x86_64	OK	146
windows-release-arm64	OK	237
windows-release-x86_64	OK	110
windows-oldrel-x86_64	OK	104
wasm-release	OK	104

Exports:output_to_df WH

Dependencies:Rcpp

Revisiting Whittaker-Henderson Smoothing

Rendered fromWH.Rmdusingknitr::rmarkdown

Last update: 2025-06-19
Started: 2022-08-16

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
WH : Enhanced Implementation of Whittaker-Henderson Smoothing	WH-package
Store WH model fit results in a data.frame	output_to_df
Plot 1D WH fit	plot.WH_1d
Plot 2D WH fit	plot.WH_2d
Aggregated Long-Term Care Dataset	portfolio_LTC
Aggregated Mortality Dataset	portfolio_mort
Predict new values using a fitted 1D WH model	predict.WH_1d
Predict new values using a fitted 2D WH model	predict.WH_2d
Display of 1D WH object	print.WH_1d
Display of 2D WH object	print.WH_2d
Compute variance-covariance matrix of fitted 1D WH model	vcov.WH_1d
Compute variance-covariance matrix of fitted 1D WH model	vcov.WH_2d
Whittaker-Henderson Smoothing	WH