The function for calculating the adjusted pvalues based on original available pvalues and all attaianble pvalues
1  MBonf.p.adjust(p, p.set)

p 
numeric vector of pvalues (possibly with 
p.set 
a list of numeric vectors, where each vector is the vector of all attainable pvalues containing the available pvalue for the corresponding hypothesis. 
A numeric vector of the adjusted pvalues (of the same length as p
).
The attainable pvalue refers to the element of domain set of pvalue for the corresponding hypothesis. For continuous test statistics, the pvalue under true null are uniform distributed in (0,1), thus the pvalues are attainable everywhere between 0 and 1. But for discrete test statistics, the pvalue can only take finite values bewtween 0 and 1, that is the attainable pvalues for discrete case are finite and countable, so we can assign them in a finite list p.set
.
Yalin Zhu
Tarone.p.adjust
, MixBonf.p.adjust
, p.adjust
.
1 2 3 
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