c(person(given = "Florian",
family = "Laroumagne",
role = c("aut", "cre"),
email = "florian.laroumagne@prevision.io"),
person(given = "Prevision.io Inc",
role = "cph",
email = "support@prevision.io"))
For working with the 'Prevision.io' AI model management platform's API <https://prevision.io/>.
data.table, futile.logger, httr, jsonlite, Metrics, graphics,
stats, utils, XML, magrittr, plotly
Florian Laroumagne [aut, cre],
Prevision.io Inc [cph]

Prevalence to Incidence Calculations for Point-Prevalence
Studies in a Nosocomial Setting
person("Niklas", "Willrich", email = "WillrichN@rki.de", role = c("aut", "cre"))
Functions to simulate point prevalence studies (PPSs) of healthcare-associated infections (HAIs) and to convert prevalence to incidence in steady state setups.
Companion package to the preprint Willrich et al., From prevalence to incidence - a new approach in the hospital setting;
<doi:10.1101/554725> , where methods are explained in detail.
knitr, rmarkdown, testthat, ggplot2, purrr, gridExtra, tidyr

knitr, rmarkdown, testthat, ggplot2, gridExtra, tidyr

Data Pre-Processing for Bacterial Genome-Wide Association
Studies
c(
person("Katie", "Saund",
email = "katiephd@umich.edu",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6214-6713")),
person("Zena", "Lapp",
email = "zenalapp@umich.edu",
role = "aut",
comment = c(ORCID = "0000-0003-4674-2176")),
person("Stephanie", "Thiede",
email = "thiede@umich.edu",
role = "aut",
comment = c(ORCID = "0000-0003-0173-4324")))
Standardize the pre-processing of genomic variants before
performing a bacterial genome-wide association study (bGWAS). prewas creates
a variant matrix (where each row is a variant, each column is a sample, and
the entries are presence - 1 - or absence - 0 - of the variant) that can be
used as input for bGWAS tools. When creating the binary variant matrix,
prewas can perform 3 pre-processing steps including: dealing with
multiallelic SNPs, (optional) dealing with SNPs in overlapping genes, and
choosing a reference allele. prewas can output matrices for use with both
SNP-based bGWAS and gene-based bGWAS. This method is described in Saund et
al. (2019) <doi:10.1101/2019.12.20.873158>.