glmFilter()lmFilter() now supports eigenvector selection based on
AIC, AICc, and BIClmFilter()lmFilter() now allows to compute conditional standard
errors for regression coefficients using a partial regression
framework.glmFilter() in future releasesvp()MI.vec() and
MI.decomp():
MI.resid()glmFilter() now supports ‘nb’ (for negative binomial)
as model typeglmFilter() also provides McFadden’s adjusted pseudo
R-squared for the filtered vs. the unfiltered modelMI.local() and
MI.vec() functionslmFilter() and
glmFilter() occurring when covariates are supplied as
data.frameMI.vec(),
MI.decomp(), and MI.local()lmFilter()
functionMI.local() function to calculate local Moran’s Ivp() function for variation partitioningMI.local() in documentation
fileslmFilter()
functionMI.local() function to calculate local Moran’s Ivp() function for variation partitioningMI.local() in documentation
filesMI.decomp() to decompose Moran’s IMI.resid()lmFilter() and glmFilter() now also
support unsupervised eigenvector selection based on the significance of
residual autocorrelation
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