c(person(given = "Stefan Milton",
family = "Bache",
role = c("aut", "cph"),
email = "stefan@stefanbache.dk",
comment = "Original author and creator of magrittr"),
person(given = "Hadley",
family = "Wickham",
role = "aut",
email = "hadley@rstudio.com"),
person(given = "Lionel",
family = "Henry",
role = "cre",
email = "lionel@rstudio.com"),
person(given = "RStudio",
role = c("cph", "fnd")))
Provides a mechanism for chaining commands with a
new forward-pipe operator, %>%. This operator will forward a value, or
the result of an expression, into the next function call/expression.
There is flexible support for the type of right-hand side expressions.
For more information, see package vignette.  To quote Rene Magritte,
"Ceci n'est pas un pipe."
Stefan Milton Bache [aut, cph] (Original author and creator of
magrittr),
Hadley Wickham [aut],
Lionel Henry [cre],
RStudio [cph, fnd]

c(
person("Stefan Milton", "Bache", , "stefan@stefanbache.dk", role = c("aut", "cph"),
comment = "Original author and creator of magrittr"),
person("Hadley", "Wickham", , "hadley@rstudio.com", role = "aut"),
person("Lionel", "Henry", , "lionel@rstudio.com", role = "cre"),
person("RStudio", role = c("cph", "fnd"))
)
Provides a mechanism for chaining commands with a new
forward-pipe operator, %>%. This operator will forward a value, or the
result of an expression, into the next function call/expression.
There is flexible support for the type of right-hand side expressions.
For more information, see package vignette.  To quote Rene Magritte,
"Ceci n'est pas un pipe."

c(person("Rob", "Young", role = c("aut", "cre"),
email = "rob.young@heor.co.uk"))
A generalised workflow for generation of subject weights to be
used in Matching-Adjusted Indirect Comparison (MAIC) per Signorovitch et
al (2012) <doi:10.1016/j.jval.2012.05.004>, Signorovitch et al (2010)
<doi:10.2165/11538370-000000000-00000>. In MAIC, unbiased
comparison between outcomes of two trials is facilitated by weighting the
subject-level outcomes of one trial with weights derived such that the
weighted aggregate measures of the prognostic or effect modifying variables
are equal to those of the sample in the comparator trial. The functions and
classes included in this package wrap and abstract the process demonstrated
in the UK National Institute for Health and Care Excellence Decision
Support Unit (NICE DSU)'s example (Phillippo et al, (2016) [see URL]),
providing a repeatable and easily specifiable workflow for producing
multiple comparison variable sets against a variety of target studies, with
preprocessing for a number of aggregate target forms (e.g. mean, median,
domain limits).