This package is considered a duplicate. The official version of this package is found at:https://cran.r-universe.dev/incidental

Package: incidental 0.1

Lauren Hannah

incidental: Implements Empirical Bayes Incidence Curves

Make empirical Bayes incidence curves from reported case data using a specified delay distribution.

Authors:Andrew Miller [aut], Lauren Hannah [aut, cre], Nicholas Foti [aut], Joseph Futoma [aut], Apple, Inc. [cph]

incidental_0.1.tar.gz
incidental_0.1.zip(r-4.7-any)incidental_0.1.zip(r-4.6-any)incidental_0.1.zip(r-4.5-any)
incidental_0.1.tgz(r-4.6-any)incidental_0.1.tgz(r-4.5-any)
incidental_0.1.tar.gz(r-4.7-any)incidental_0.1.tar.gz(r-4.6-any)
incidental_0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
incidental/json (API)

# Install 'incidental' in R:

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

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

Datasets:

covid_delay_dist - Delay distribution from COVID-19 pandemic.
covid_new_york_city - New York City data from the COVID-19 pandemic.
spanish_flu - Daily flu mortality from 1918 flu pandemic.
spanish_flu_delay_dist - Delay distribution from 1918 flu pandemic.

On CRAN:

archived packages r-universe

3.40 score 5 stars 10 scripts 298 downloads 24 exports 26 dependencies

Last updated from:e370933c25 (on package/incidental). Checks:7 NOTE, 2 OK. Indexed: no.

Target	Result	Time
linux-devel	NOTE	145
source / vignettes	OK	195
linux-release	NOTE	138
macos-release	NOTE	107
macos-oldrel	NOTE	123
windows-devel	NOTE	107
windows-release	NOTE	85
windows-oldrel	NOTE	100
wasm-release	OK	89

Exports:compute_expected_cases compute_log_incidence data_check data_processing fit_incidence front_zero_pad incidence_to_df init_params make_ar_extrap_samps make_likelihood_matrix make_spline_basis marg_loglike_poisson marg_loglike_poisson_fisher marg_loglike_poisson_grad poisson_objective poisson_objective_grad regfun regfun_grad regfun_hess sample_laplace_log_incidence_poisson scan_spline_dof scan_spline_lam train_and_validate train_val_split

Dependencies:cli cpp11 dlnm farver ggplot2 glue gtable isoband labeling lattice lifecycle MASS Matrix matrixStats mgcv nlme numDeriv R6 RColorBrewer rlang S7 scales tsModel vctrs viridisLite withr

incidental-tutorial

Rendered fromincidental-tutorial.Rmdusingknitr::rmarkdown

Last update: 2026-10-12
Started: 2026-10-12

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Compute expected cases	compute_expected_cases
Compute log likelihood of incidence model	compute_log_incidence
Delay distribution from COVID-19 pandemic.	covid_delay_dist
New York City data from the COVID-19 pandemic.	covid_new_york_city
Input data check	data_check
Data processing wrapper	data_processing
Transpose of the 1st difference operator	diff_trans
Fit incidence curve to reported data	fit_incidence
Pad reported data with zeros in front	front_zero_pad
Export incidence model to data frame	incidence_to_df
Initialize spline parameters (beta)	init_params
Make AR samples for extrapolation past end point	make_ar_extrap_samps
Make delay likelihood matrix	make_likelihood_matrix
Create spline basis matrix	make_spline_basis
Marginal log likelihood This function computes the marginal probability of Pr(reported \| beta). Note that lnPmat must be zero padded enough (or censored) to match the length of reported cases vector.	marg_loglike_poisson
Marginal log likelihood Fisher information matrix	marg_loglike_poisson_fisher
Marginal log likelihood gradient	marg_loglike_poisson_grad
Plot model from fit_incidence	plot.incidence_spline_model
Poisson objective function	poisson_objective
Poisson objective function gradient	poisson_objective_grad
Compute Fisher information matrix for Poisson objective	poisson_objective_post_cov_approx
Beta regularization function	regfun
Beta regularization function gradient	regfun_grad
Beta regularization function Hessian	regfun_hess
Generate Laplace samples of incidence	sample_laplace_log_incidence_poisson
Scan spline degrees of freedom	scan_spline_dof
Scan spline regularization parameter	scan_spline_lam
Daily flu mortality from 1918 flu pandemic.	spanish_flu
Delay distribution from 1918 flu pandemic.	spanish_flu_delay_dist
Train and validate model on reported data	train_and_validate
Split reported case data	train_val_split