Package: epidist 0.4.0

Sam Abbott

epidist: Estimate Epidemiological Delay Distributions With brms

Understanding and accurately estimating epidemiological delay distributions is important for public health policy. These estimates influence epidemic situational awareness, control strategies, and resource allocation. This package provides methods to address the key challenges in estimating these distributions, including truncation, interval censoring, and dynamical biases. These issues are frequently overlooked, resulting in biased conclusions. Built on top of 'brms', it allows for flexible modelling including time-varying spatial components and partially pooled estimates of demographic characteristics.

Authors:Adam Howes [aut], Sang Woo Park [aut], Sam Abbott [aut, cre], Sebastian Funk [ctb]

epidist_0.4.0.tar.gz
epidist_0.4.0.zip(r-4.7)epidist_0.4.0.zip(r-4.6)epidist_0.4.0.zip(r-4.5)
epidist_0.4.0.tgz(r-4.6-any)epidist_0.4.0.tgz(r-4.5-any)
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epidist_0.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
epidist/json (API)

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

Bug tracker:https://github.com/epinowcast/epidist/issues

Pkgdown/docs site:https://epidist.epinowcast.org

Datasets:

On CRAN:

Conda:

4.82 score 16 stars 13 scripts 42 exports 78 dependencies

Last updated from:be82d80d13 (on v0.4.0). Checks:8 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR259
source / vignettesERROR328
linux-release-x86_64ERROR239
macos-release-arm64ERROR172
macos-oldrel-arm64ERROR172
windows-develERROR205
windows-releaseERROR236
windows-oldrelERROR256
wasm-releaseOK202

Exports:add_mean_sdas_epidist_aggregate_dataas_epidist_latent_modelas_epidist_linelist_dataas_epidist_marginal_modelas_epidist_naive_modelassert_epidistbfepidistepidist_diagnosticsepidist_familyepidist_family_modelepidist_family_paramepidist_family_priorepidist_formulaepidist_formula_modelepidist_gen_posterior_epredepidist_gen_posterior_predictepidist_model_priorepidist_priorepidist_stancodeepidist_transform_dataepidist_transform_data_modelGammais_epidist_aggregate_datais_epidist_latent_modelis_epidist_linelist_datais_epidist_marginal_modelis_epidist_naive_modellognormalnew_epidist_aggregate_datanew_epidist_latent_modelnew_epidist_linelist_datanew_epidist_marginal_modelnew_epidist_naive_modelpredict_delay_parameterspredict_dparsimulate_exponential_casessimulate_gillespiesimulate_secondarysimulate_uniform_casesweibull

Dependencies:abindbackportsbayesplotBHbridgesamplingbrmsBrobdingnagcallrcheckmateclicodacodetoolscpp11descdigestdistributionaldplyrfarverfuturefuture.applygenericsggplot2ggridgesglobalsgluegridExtragtableinlineisobandlabelinglatticelifecyclelistenvloolubridatemagrittrMatrixmatrixStatsmgcvmvtnormnleqslvnlmenumDerivotelparallellypillarpkgbuildpkgconfigplyrposteriorprimarycensoredprocessxpspurrrQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanrstantoolsS7scalesStanHeadersstringistringrtensorAtibbletidyrtidyselecttimechangeutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Add natural scale mean and standard deviation parametersadd_mean_sd
Default method for add natural scale parametersadd_mean_sd.default
Add natural scale mean and standard deviation parameters for a Gamma modeladd_mean_sd.gamma_samples
Add natural scale mean and standard deviation parameters for a lognormal modeladd_mean_sd.lognormal_samples
Add natural scale mean and standard deviation parameters for a Weibull modeladd_mean_sd.weibull_samples
Create an epidist_aggregate_data objectas_epidist_aggregate_data
Create an epidist_aggregate_data object from a data.frameas_epidist_aggregate_data.data.frame
Create an epidist_aggregate_data object from vectors of event timesas_epidist_aggregate_data.default
Convert linelist data to aggregate formatas_epidist_aggregate_data.epidist_linelist_data
Convert an object to an 'epidist_latent_model' objectas_epidist_latent_model
The latent model method for 'epidist_aggregate_data' objectsas_epidist_latent_model.epidist_aggregate_data
The latent model method for 'epidist_linelist_data' objectsas_epidist_latent_model.epidist_linelist_data
Create an epidist_linelist_data objectas_epidist_linelist_data
Create an epidist_linelist_data object from a data frame with event datesas_epidist_linelist_data.data.frame
Create an epidist_linelist_data object from vectors of event timesas_epidist_linelist_data.default
Convert aggregate data to linelist formatas_epidist_linelist_data.epidist_aggregate_data
Convert an object to an 'epidist_marginal_model' objectas_epidist_marginal_model
The marginal model method for 'epidist_aggregate_data' objectsas_epidist_marginal_model.epidist_aggregate_data
The marginal model method for 'epidist_linelist_data' objectsas_epidist_marginal_model.epidist_linelist_data
Convert an object to an 'epidist_naive_model' objectas_epidist_naive_model
The naive model method for 'epidist_aggregate_data' objectsas_epidist_naive_model.epidist_aggregate_data
The naive model method for 'epidist_linelist_data' objectsas_epidist_naive_model.epidist_linelist_data
Validation for epidist objectsassert_epidist
Assert validity of 'epidist_aggregate_data' objectsassert_epidist.epidist_aggregate_data
Assert validity of 'epidist_linelist_data' objectsassert_epidist.epidist_linelist_data
Fit epidemiological delay distributions using a 'brms' interfaceepidist
Diagnostics for 'epidist_fit' modelsepidist_diagnostics
Define 'epidist' familyepidist_family
The model-specific parts of an 'epidist_family()' callepidist_family_model epidist_formula_model
Default method for defining a model specific familyepidist_family_model.default
Create the model-specific component of an 'epidist' custom familyepidist_family_model.epidist_latent_model
Create the model-specific component of an 'epidist' custom familyepidist_family_model.epidist_marginal_model
Reparameterise an 'epidist' family to align 'brms' and Stanepidist_family_param
Default method for families which do not require a reparameterisationepidist_family_param.default
Family specific prior distributionsepidist_family_prior
Default family specific prior distributionsepidist_family_prior.default
Family specific prior distributions for the lognormal familyepidist_family_prior.lognormal
Define a model specific formulaepidist_formula
Default method for defining a model specific formulaepidist_formula_model.default
Define the model-specific component of an 'epidist' custom formula for the latent modelepidist_formula_model.epidist_latent_model
Define the model-specific component of an 'epidist' custom formula for the marginal modelepidist_formula_model.epidist_marginal_model
Define the model-specific component of an 'epidist' custom formula for the naive modelepidist_formula_model.epidist_naive_model
Create a function to calculate the marginalised log likelihood for double censored and truncated delay distributionsepidist_gen_log_lik
Create a function to draw from the expected value of the posterior predictive distribution for a modelepidist_gen_posterior_epred
Create a function to draw from the posterior predictive distribution for a double censored and truncated delay distributionepidist_gen_posterior_predict
Model specific prior distributionsepidist_model_prior
Default model specific prior distributionsepidist_model_prior.default
Model specific prior distributions for latent modelsepidist_model_prior.epidist_latent_model
Define custom prior distributions for epidist modelsepidist_prior
Define model specific Stan codeepidist_stancode
Default method for defining model specific Stan codeepidist_stancode.default
Transform data for an epidist modelepidist_transform_data
The model-specific parts of an 'epidist_transform_data()' callepidist_transform_data_model
Default method for transforming data for a modelepidist_transform_data_model.default
Transform data for the marginal modelepidist_transform_data_model.epidist_marginal_model
Transform data for the naive modelepidist_transform_data_model.epidist_naive_model
Check if data has the 'epidist_aggregate_data' classis_epidist_aggregate_data
Check if data has the 'epidist_latent_model' classis_epidist_latent_model
Check if data has the 'epidist_linelist_data' classis_epidist_linelist_data
Check if data has the 'epidist_marginal_model' classis_epidist_marginal_model
Check if data has the 'epidist_naive_model' classis_epidist_naive_model
Class constructor for 'epidist_aggregate_data' objectsnew_epidist_aggregate_data
Class constructor for 'epidist_latent_model' objectsnew_epidist_latent_model
Class constructor for 'epidist_linelist_data' objectsnew_epidist_linelist_data
Class constructor for 'epidist_marginal_model' objectsnew_epidist_marginal_model
Class constructor for 'epidist_naive_model' objectsnew_epidist_naive_model
Extract samples of the delay distribution parameterspredict_delay_parameters predict_dpar
Ebola linelist data from Fang et al. (2016)sierra_leone_ebola_data
Simulate exponential casessimulate_exponential_cases
Simulate cases from a stochastic SIR modelsimulate_gillespie
Simulate secondary events based on a delay distributionsimulate_secondary
Simulate cases from a uniform distributionsimulate_uniform_cases