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.tar.gz(r-4.5-noble)incidental_0.1.tar.gz(r-4.4-noble)
incidental_0.1.tgz(r-4.4-emscripten)incidental_0.1.tgz(r-4.3-emscripten)
incidental.pdf |incidental.html✨
incidental/json (API)
NEWS

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

Peer review:

Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 2 stars 8 scripts 147 downloads 24 exports 32 dependencies

Last updated 4 years agofrom:70cac274ab. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-linuxNOTENov 21 2024

Exports:compute_expected_casescompute_log_incidencedata_checkdata_processingfit_incidencefront_zero_padincidence_to_dfinit_paramsmake_ar_extrap_sampsmake_likelihood_matrixmake_spline_basismarg_loglike_poissonmarg_loglike_poisson_fishermarg_loglike_poisson_gradpoisson_objectivepoisson_objective_gradregfunregfun_gradregfun_hesssample_laplace_log_incidence_poissonscan_spline_dofscan_spline_lamtrain_and_validatetrain_val_split

Dependencies:clicolorspacedlnmfansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgconfigR6RColorBrewerrlangscalestibbletsModelutf8vctrsviridisLitewithr

incidental-tutorial

Rendered fromincidental-tutorial.Rmdusingknitr::rmarkdownon Nov 21 2024.

Last update: 2020-09-16
Started: 2020-09-16

Readme and manuals

Help Manual

Help pageTopics
Compute expected casescompute_expected_cases
Compute log likelihood of incidence modelcompute_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 checkdata_check
Data processing wrapperdata_processing
Transpose of the 1st difference operatordiff_trans
Fit incidence curve to reported datafit_incidence
Pad reported data with zeros in frontfront_zero_pad
Export incidence model to data frameincidence_to_df
Initialize spline parameters (beta)init_params
Make AR samples for extrapolation past end pointmake_ar_extrap_samps
Make delay likelihood matrixmake_likelihood_matrix
Create spline basis matrixmake_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 matrixmarg_loglike_poisson_fisher
Marginal log likelihood gradientmarg_loglike_poisson_grad
Plot model from fit_incidenceplot.incidence_spline_model
Poisson objective functionpoisson_objective
Poisson objective function gradientpoisson_objective_grad
Compute Fisher information matrix for Poisson objectivepoisson_objective_post_cov_approx
Beta regularization functionregfun
Beta regularization function gradientregfun_grad
Beta regularization function Hessianregfun_hess
Generate Laplace samples of incidencesample_laplace_log_incidence_poisson
Scan spline degrees of freedomscan_spline_dof
Scan spline regularization parameterscan_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 datatrain_and_validate
Split reported case datatrain_val_split