c(
person("Christopher", "Gandrud", email = "zelig.zee@gmail.com",
role = c("aut", "cre")),
person("Gary", "King", role = "aut"),
person("Heather", "Stoll", role = "aut"),
person("Langche", "Zeng", role = "aut")
)
Inferences about counterfactuals are essential for prediction,
answering what if questions, and estimating causal effects.
However, when the counterfactuals posed are too far from the data at
hand, conclusions drawn from well-specified statistical analyses
become based largely on speculation hidden in convenient modeling
assumptions that few would be willing to defend. Unfortunately,
standard statistical approaches assume the veracity of the model
rather than revealing the degree of model-dependence, which makes this
problem hard to detect. WhatIf offers easy-to-apply methods to
evaluate counterfactuals that do not require sensitivity testing over
specified classes of models. If an analysis fails the tests offered
here, then we know that substantive inferences will be sensitive to at
least some modeling choices that are not based on empirical evidence,
no matter what method of inference one chooses to use.
Christopher Gandrud [aut, cre],
Gary King [aut],
Heather Stoll [aut],
Langche Zeng [aut]

c(
person("Christopher", "Gandrud", email = "zelig.zee@gmail.com",
role = c("aut", "cre")),
person("Gary", "King", role = "aut"),
person("Ben", "Sabath", role = "ctb"),
person("Heather", "Stoll", role = "aut"),
person("Langche", "Zeng", role = "aut")
)
Christopher Gandrud [aut, cre],
Gary King [aut],
Ben Sabath [ctb],
Heather Stoll [aut],
Langche Zeng [aut]

Whitestripe White Matter Normalization for Magnetic Resonance
Images
Whitestripe normalization from Shinohara et. al does For
intensity-based normalization of T1 and T2 images, where normal appearing
white matter performs well, but requires segmentation.  This method
performs white matter mean and standard deviation estimates on data that
has been rigidly-registered to the MNI template and uses histogram-based
methods.

White Matter Normalization for Magnetic Resonance Images using
Whitestripe
Shinohara et. al introduced Whitestripe, an
intensity-based normalization of T1 and T2 images, where normal
appearing white matter performs well, but requires segmentation.
This method performs white matter mean and standard deviation
estimates on data that has been rigidly-registered to the MNI
template and uses histogram-based methods.