I've been doing some more complex experiment designs, using "block" and "correlation" arguments to limma::lmFit. This is easily accommodated by allowing the pass-through of optional arguments using ...:
runLimmaAnalysis <- function (dat, groups = NULL, base.group = NULL, design = NULL, ...)
#...
fit <- limma::lmFit(dat.limma, design=design, ...)
#...
I've been doing some more complex experiment designs, using "block" and "correlation" arguments to limma::lmFit. This is easily accommodated by allowing the pass-through of optional arguments using
...
: