c(
person("Jon", "Lefcheck",   role = c("aut", "cre"), email = "lefcheckj@si.edu"),
person("Jarrett", "Byrnes",  role = "aut", email = "jarrett.byrnes@umb.edu"),
person("James", "Grace",  role = "aut", email = "gracej@usgs.gov")
)
car, DiagrammeR, emmeans, igraph, lme4, MASS, methods, nlme

car, DiagrammeR, emmeans, igraph, lme4, multcomp, MASS,
methods, nlme

Potential Impact Fraction and Population Attributable Fraction
for Cross-Sectional Data
c(person("Rodrigo", "Zepeda-Tello", email = "rzepeda17@gmail.com", role = c("aut", "cre")), person("Dalia", "Camacho-García-Formentí", email = "daliaf172@gmail.com", role = c("aut")), person("Tonatiuh","Barrientos-Gutiérrez", role = c("ctb")), person("Ana","Basto-Abreu", role = c("ctb")), person("Ariela","Braverman-Bronstein", role = c("ctb")), person("Dèsirée","Vidaña-Pérez", role = c("ctb")), person("Frederick","Cudhea", role = c("ctb")), person("Instituto Nacional de Salud Pública", role=c("cph")))
Uses a generalized method to estimate the Potential Impact Fraction (PIF) and the Population Attributable Fraction (PAF) from cross-sectional data. It creates point-estimates, confidence intervals, and estimates of variance. In addition it generates plots for conducting sensitivity analysis. The estimation method corresponds to  Zepeda-Tello,  Camacho-García-Formentí, et al. 2017. 'Nonparametric Methods to Estimate the Potential Impact Fraction from Cross-sectional Data'. Unpublished manuscript. This package was developed under funding by Bloomberg Philanthropies.
ggplot2, matrixcalc, MASS, sfsmisc, stats, gridExtra, numDeriv
Rodrigo Zepeda-Tello [aut, cre],
Dalia Camacho-García-Formentí [aut],
Tonatiuh Barrientos-Gutiérrez [ctb],
Ana Basto-Abreu [ctb],
Ariela Braverman-Bronstein [ctb],
Dèsirée Vidaña-Pérez [ctb],
Frederick Cudhea [ctb],
Instituto Nacional de Salud Pública [cph]

Managing Larger Data on a GitHub Repository
Because larger (> 50 MB) data files cannot easily be committed to git,
a different approach is required to manage data associated with an analysis in a
GitHub repository.  This package provides a simple work-around by allowing larger
(up to 2 GB) data files to piggyback on a repository as assets attached to individual
GitHub releases.  These files are not handled by git in any way, but instead are
uploaded, downloaded, or edited directly by calls through the GitHub API. These
data files can be versioned manually by creating different releases.  This approach
works equally well with public or private repositories.  Data can be uploaded
and downloaded programmatically from scripts. No authentication is required to
download data from public repositories.
c(person("Carl", "Boettiger",
email = "cboettig@gmail.com",
role = c("aut", "cre", "cph"),
comment = c(ORCID = "0000-0002-1642-628X")),
person("Mark", "Padgham",
role = c("ctb"),
comment = c(ORCID = "0000-0003-2172-5265")),
person(c('Jeffrey', 'O'), 'Hanson',
role = c("ctb"),
comment = c(ORCID = "0000-0002-4716-6134")))
gh, httr, jsonlite, git2r, fs, usethis, crayon, clisymbols,
magrittr, lubridate, memoise
spelling, readr, covr, testthat, datasets, knitr, rmarkdown
Carl Boettiger [aut, cre, cph]
(<https://orcid.org/0000-0002-1642-628X>),
Mark Padgham [ctb] (<https://orcid.org/0000-0003-2172-5265>),
Jeffrey O Hanson [ctb] (<https://orcid.org/0000-0002-4716-6134>)