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wls
(weighted least squares) in function names is now
replaced with gls
(generalised least squars) to more
properly reflect the scope of the functionality. For example, the
function wlsPower()
is now called glsPower()
-
although the former version still works and throws a warning.compute_InfoContent()
plot.glsPower()
there now is an option to manually
set the font size of the annotation in the influence plotswlsPower()
now also computes the
information content of cluster-period cells. Computation is currently
done twice, once with a general formula and once explicitly. Information
content of whole periods or clusters is also computed.plot.wlsPower()
recieved multiple updates:
annotations = <TRUE/FALSE>
show_colorbar
to hide colour bars was
addedmarginal_plots
to hide marginal plots on
whole periods or clusters was added.wlsPower()
now has an argument
alpha_012
that offers an alternative way to specifiy the
correlation matrix.wlsPower()
, the argument AR
now accepts a vector of up to three values. This allows to specifiy
autoregressive structures for only a subset of: random cluster
intercept, random intervention effect and random subject intercept.plot.wlsPower
now produces up to three
plots, the projection matrix, the intervention design and the covariance
matrix.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.