c(
person("Robert", "Nuske", role=c("aut", "cre"), email="robert.nuske@mailbox.org",
comment=c(ORCID="0000-0001-9773-2061")),
person("Dirk", "Eddelbuettel", role="aut"),
person("Aaron", "Wolen", role="aut"))

Simply Beautiful PDF Letters from Markdown
c(
person("Robert", "Nuske", role=c("aut", "cre"),
email="robert.nuske@nw-fva.de",
comment=c(ORCID="0000-0001-9773-2061")),
person("Dirk", "Eddelbuettel", role="aut",
comment=c(ORCID="0000-0001-6419-907X")),
person("Aaron", "Wolen", role="aut",
comment=c(ORCID="0000-0003-2542-2202")))
Write beautiful yet customizable letters in R Markdown and
directly obtain the finished PDF. Smooth generation of PDFs is realized by
'rmarkdown', the 'pandoc-letter' template and the 'KOMA-Script' letter class.
'KOMA-Script' provides enhanced replacements for the standard 'LaTeX' classes
with emphasis on typography and versatility. 'KOMA-Script' is particularly
useful for international writers as it handles various paper formats well,
provides layouts for many common window envelope types (e.g. German, US,
French, Japanese) and lets you define your own layouts. The package comes
with a default letter layout based on 'DIN 5008B'.

Write beautiful yet versatile letters in R Markdown. PDFs are
generated using the 'KOMA-Script' letter class and the 'pandoc-letter'
template. 'KOMA-Script' caters to the international writer. It provides
layouts for many common window envelope types (e.g. German, US, French,
Japanese) and allows you to define your own. The package comes with a default
layout based on 'DIN 5008B'.

Quantify the Robustness of Causal Inferences
c(
person(c("Joshua", "M"), "Rosenberg", role = c("aut", "cre"), email = "jmichaelrosenberg@gmail.com"),
person(c("Ran"), "Xu", role = "ctb", email = "ranxu@msu.edu"),
person(c("Kenneth", "A"), "Frank", role = "ctb", email = "kenfrank@msu.edu")
)
Statistical methods that quantify the conditions necessary to alter
inferences, also known as sensitivity analysis, are becoming increasingly
important to a variety of quantitative sciences. A series of recent works,
including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al.
(2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses
by considering the characteristics of omitted variables or unobserved cases
that would change an inference if such variables or cases were observed. These
analyses generate statements such as "an omitted variable would have to be
correlated at xx with the predictor of interest (e.g., treatment) and outcome
to invalidate an inference of a treatment effect". Or "one would have to replace
pp percent of the observed data with null hypothesis cases to invalidate the
inference". We implement these recent developments of sensitivity analysis and
provide modules to calculate these two robustness indices and generate such
statements in R. In particular, the functions konfound(), pkonfound() and
mkonfound() allow users to calculate the robustness of inferences for a user's
own model, a single published study and multiple studies respectively.
broom, dplyr, ggplot2, margins, pbkrtest, purrr, rlang, tidyr
devtools, forcats, knitr, lme4, rmarkdown, roxygen2, testthat
Joshua M Rosenberg [aut, cre],
Ran Xu [ctb],
Kenneth A Frank [ctb]