Package: anovir 0.1.0

anovir: Analysis of Virulence

Epidemiological population dynamics models traditionally define a pathogen's virulence as the increase in the per capita rate of mortality of infected hosts due to infection. This package provides functions allowing virulence to be estimated by maximum likelihood techniques. The approach is based on the analysis of relative survival comparing survival in matching cohorts of infected vs. uninfected hosts (Agnew 2019) <doi:10.1101/530709>.

Authors:Philip Agnew [aut, cre], Jimmy Lopez [aut]

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anovir/json (API)

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

Peer review:

Datasets:

On CRAN:

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

3.34 score 20 scripts 235 downloads 20 exports 7 dependencies

Last updated 4 years agofrom:80ffca0c6b. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 22 2024
R-4.5-winOKNov 22 2024
R-4.5-linuxOKNov 22 2024
R-4.4-winOKNov 22 2024
R-4.4-macOKNov 22 2024
R-4.3-winOKNov 22 2024
R-4.3-macOKNov 22 2024

Exports:av_long_infectedav_long_uninfectedcheck_dataconf_ints_virulenceetd_infectedetd_uninfectednll_basicnll_basic_logscalenll_controlsnll_exposed_infectednll_frailtynll_frailty_correlatednll_frailty_logscalenll_frailty_sharednll_proportional_virulencenll_recoverynll_recovery_IInll_two_inf_subpops_obsnll_two_inf_subpops_unobssim_data_nll_basic

Dependencies:bbmlebdsmatrixlatticeMASSMatrixmvtnormnumDeriv

Confidence intervals

Rendered fromconfidence_intervals.Rmdusingknitr::rmarkdownon Nov 22 2024.

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Data format

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Introduction

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Likelihood functions described

Rendered fromnll_functions.Rmdusingknitr::rmarkdownon Nov 22 2024.

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Modifying nll functions

Rendered fromnll_functions_modifying.Rmdusingknitr::rmarkdownon Nov 22 2024.

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Probability distribution functions

Rendered fromprobability_distributions.Rmdusingknitr::rmarkdownon Nov 22 2024.

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Starting values

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The exponential distribution

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Using nll functions

Rendered fromnll_functions_using.Rmdusingknitr::rmarkdownon Nov 22 2024.

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Worked examples I

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Worked examples II

Rendered fromworked_examples_II.Rmdusingknitr::rmarkdownon Nov 22 2024.

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Readme and manuals

Help Manual

Help pageTopics
anovir: Analysis of Virulenceanovir-package anovir
Average longevity: estimate for infected hostsav_long_infected
Average longevity: estimate for uninfected hostsav_long_uninfected
Checks data are correctly described for modelscheck_data
Approximate 95% confidence intervals for virulenceconf_ints_virulence
Full data from Blanford et al (2012)data_blanford
A subset of data from Lorenz & Koella (2011)data_lorenz
Full data from Parker et al (2014)data_parker
Expected time of death: infected hostsetd_infected
Expected time of death: uninfected hostsetd_uninfected
Negative log-likelihood function: basic modelnll_basic
Negative log-likelihood function: basic model on logscalenll_basic_logscale
Negative log-likelihood function: control data onlynll_controls
Negative log-likelihood function: exposed-infectednll_exposed_infected
Negative log-likelihood function: frailtynll_frailty
Negative log-likelihood function: correlated frailty modelnll_frailty_correlated
Negative log-likelihood function: frailty variables on logscalenll_frailty_logscale
Negative log-likelihood function: frailty sharednll_frailty_shared
Negative log-likelihood function: nll proportional virulencenll_proportional_virulence
Negative log-likelihood function: recovery modelnll_recovery
Negative log-likelihood function: recovery model, no background mortalitynll_recovery_II
Negative log-likelihood function: two observed subpopulations of infected hostsnll_two_inf_subpops_obs
Negative log-likelihood function: two unobserved subpopulations of infected hostsnll_two_inf_subpops_unobs
Simulated recovery datarecovery_data
Simulated recovery data, with no background mortalityrecovery_data_II
Function simulating survival data for nll_basicsim_data_nll_basic