* using log directory 'd:/Rcompile/CRANpkg/local/4.4/imprinting.Rcheck' * using R version 4.4.2 (2024-10-31 ucrt) * using platform: x86_64-w64-mingw32 * R was compiled by gcc.exe (GCC) 13.3.0 GNU Fortran (GCC) 13.3.0 * running under: Windows Server 2022 x64 (build 20348) * using session charset: UTF-8 * checking for file 'imprinting/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'imprinting' version '0.1.1' * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'imprinting' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... 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[1s] NOTE checkRd: (-1) get_country_cocirculation_data.Rd:43: Lost braces 43 | \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)} for detailed methods. | ^ checkRd: (-1) get_country_intensity_data.Rd:20: Lost braces 20 | \verb{get_country_intensity data()} returns data on the annual intensity of influenza circulation in each calendar year. Following \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)}, we define 1 as the average intensity. Seasons with intensities greater than 1 have more flu A circulation than average, and seasons with intensities less than 1 are mild. | ^ checkRd: (-1) get_imprinting_probabilities.Rd:34: Lost braces 34 | Imprinting probabilities are calculated following \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)}. Briefly, the model first calculates the probability that an individual's first influenza infection occurs 0, 1, 2, ... 12 years after birth using a modified geometric waiting time model. The annual circulation intensities output by \code{\link[=get_country_intensity_data]{get_country_intensity_data()}} scale the probability of primary infection in each calendar year. | ^ checkRd: (-1) get_imprinting_probabilities.Rd:38: Lost braces; missing escapes or markup? 38 | To calculate other kinds of imprinting probabilities (e.g. for specific clades, strains, or to include pediatric vaccination), users can specify custom circulation frequencies as a list, \code{annual_frequencies}. This list must contain one named element for each country in the \code{countries} input vector. Each list element must be a data frame or tibble whose first column is named "year" and contains numeric years from 1918:max(\code{observation_years}). Columns 2:N of the data frame must contain circulation frequencies that sum to 1 across each row, and each column must have a unique name indicating the exposure kind. E.g. column names could be {"year", "H1N1", "H2N2", "H3N2", "vaccinated"} to include probabilities of imprinting by vaccine, or {"year", "3C.3A", "not_3C.3A"} to calculate clade-specific probabilities. Do not include a naive column. Any number of imprinting types is allowed, but the code is not optimized to run efficiently when the number of categories is very large. Frequencies within the column must be supplied by the user. See \href{https://www.nature.com/articles/s41467-021-24566-y}{Vieira et al. 2021} for methods to estimate circulation frequencies from sequence databases like \href{https://gisaid.org/}{GISAID} or the \href{https://www.ncbi.nlm.nih.gov/genomes/FLU/Database/nph-select.cgi?go=database}{NCBI Sequence Database}. | ^ checkRd: (-1) get_imprinting_probabilities.Rd:38: Lost braces; missing escapes or markup? 38 | To calculate other kinds of imprinting probabilities (e.g. for specific clades, strains, or to include pediatric vaccination), users can specify custom circulation frequencies as a list, \code{annual_frequencies}. This list must contain one named element for each country in the \code{countries} input vector. Each list element must be a data frame or tibble whose first column is named "year" and contains numeric years from 1918:max(\code{observation_years}). Columns 2:N of the data frame must contain circulation frequencies that sum to 1 across each row, and each column must have a unique name indicating the exposure kind. E.g. column names could be {"year", "H1N1", "H2N2", "H3N2", "vaccinated"} to include probabilities of imprinting by vaccine, or {"year", "3C.3A", "not_3C.3A"} to calculate clade-specific probabilities. Do not include a naive column. Any number of imprinting types is allowed, but the code is not optimized to run efficiently when the number of categories is very large. Frequencies within the column must be supplied by the user. See \href{https://www.nature.com/articles/s41467-021-24566-y}{Vieira et al. 2021} for methods to estimate circulation frequencies from sequence databases like \href{https://gisaid.org/}{GISAID} or the \href{https://www.ncbi.nlm.nih.gov/genomes/FLU/Database/nph-select.cgi?go=database}{NCBI Sequence Database}. | ^ checkRd: (-1) get_p_infection_year.Rd:24: Lost braces 24 | \item{baseline_annual_p_infection}{average annual probability of primary infection. The default, 0.28, was estimated using age-seroprevalence data in \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)}.} | ^ checkRd: (-1) get_p_infection_year.Rd:41: Lost braces 41 | This function modifies the geometric model above to account for changes in annual circulation intensity, so that annual probabilities of primary infection \eqn{p_i} are scaled by the intensity in calendar year i. Details are given in \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)}. | ^ checkRd: (-1) get_template_data.Rd:29: Lost braces 29 | \doi{https://doi.org/10.1126/science.aag1322}{Gostic et al. Science, (2016)} for detailed methods. | ^ * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... [6s] OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... [6s] OK Running 'testthat.R' [5s] * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... [31s] OK * checking PDF version of manual ... [22s] OK * checking HTML version of manual ... [2s] OK * DONE Status: 1 NOTE