Package: hpolcc 0.5.2

hpolcc: Hierarchical Air Pollution Model

Fits hierarchical functional case-crossover models for air pollution health effects, with hierarchical structures and case-crossover designs built on the adlaplace Laplace approximation framework.

Authors:Patrick Brown [aut, cre, cph]

hpolcc_0.5.2.tar.gz
hpolcc_0.5.2.zip(r-4.7)hpolcc_0.5.2.zip(r-4.6)hpolcc_0.5.2.zip(r-4.5)
hpolcc_0.5.2.tgz(r-4.6-x86_64)hpolcc_0.5.2.tgz(r-4.6-arm64)hpolcc_0.5.2.tgz(r-4.5-x86_64)hpolcc_0.5.2.tgz(r-4.5-arm64)
hpolcc_0.5.2.tar.gz(r-4.7-arm64)hpolcc_0.5.2.tar.gz(r-4.7-x86_64)hpolcc_0.5.2.tar.gz(r-4.6-arm64)hpolcc_0.5.2.tar.gz(r-4.6-x86_64)
hpolcc_0.5.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
hpolcc/json (API)

# Install 'hpolcc' in R:
install.packages('hpolcc', repos = c('https://eborgnine.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/eborgnine/adlaplace/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

4.60 score 9 exports 10 dependencies

Last updated from:abf0625826 (on main). Checks:13 OK. Indexed: yes.
A new build is currently in progress.

TargetResultTimeFilesSyslog
linux-devel-arm64OK202
linux-devel-x86_64OK219
source / vignettesOK392
linux-release-arm64OK210
linux-release-x86_64OK168
macos-release-arm64OK108
macos-release-x86_64OK271
macos-oldrel-arm64OK91
macos-oldrel-x86_64OK258
windows-develOK153
windows-releaseOK135
windows-oldrelOK128
wasm-releaseOK120

Exports:beta_infodesigndirichlet_multinomelgm_matrixhnlmprecisionrandom_inforemove_holidaystheta_info

Dependencies:adlaplaceadlaplaceHgpdata.tablelatticeMatrixRcppRCppADRcppEigentimeDatetrustOptim

Case-crossover models with hpolcc
Introduction | Data | Model specification | Fit | AD development check | Joint log density | Likelihood
Rendered fromhpolcc.Rmdusingknitr::rmarkdown

Last update: 2026-07-17
Started: 2026-07-17

Equivalence to Conditional Gamma-Poisson Model
\begin{equation}\label{eq:multinom}P(\mathbf{y} \mid \boldsymbol{\lambda}, \sum_{k=1}^K Y_k = N) | \begin{equation}\label{eq:dm}P(\mathbf{y} \mid \alpha, \boldsymbol{\mu}, N) | Simplificaiton | \begin | \begin{equation}\label{eq:dm_ratio}P(\mathbf{y} \mid \alpha, \boldsymbol{\mu}, N)
Rendered fromdirichlet_multinom.Rmdusingknitr::rmarkdown

Last update: 2026-07-17
Started: 2026-03-24

Readme and manuals

Help Manual

Help pageTopics
Dirichlet-multinomial case-crossover response termdirichlet_multinom
Dirichlet-multinomial response termbeta_info,dirichlet_multinom-method design,dirichlet_multinom-method dirichlet_multinom-class elgm_matrix,dirichlet_multinom-method precision,dirichlet_multinom-method random_info,dirichlet_multinom-method theta_info,dirichlet_multinom-method
Build observation-shard 'ad_fun_ptr' in this package's shared library.get_ad_fun_raw_obs
Build parameters-shard 'ad_fun_ptr' in this package's shared library.get_ad_fun_raw_parameters
Fit Hierarchical Non-Linear Modelshnlm
Remove Holidays from Dataremove_holidays
Summary method for 'hnlm' fitsprint.summary.hnlm summary.hnlm
Variance-covariance matrix of outer parametersvcov.hnlm