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@predict slot.glmmSeq()
and lmmSeq()glmmRefit()... in parallelisation on windows in
glmmSeq() & lmmSeq()glmmSeq() to use glmmTMB
package for fitting negative binomial GLMM (or other GLM family)
modelsplab argument to modelPlots to
customise p-value labelsmeanExp to results in
@stats$res slotlmmSeq() using
lme4::modular code. Speed increase of around 25%.glmmSeq and lmmSeqsummary function for glmmSeq and lmmSeq to display
results for an individual gene@stats slot in output objects to include
more information including DFlmmSeq using the lmerTest
packagelmmRefit function to fit an identical (g)lmer
model. This can then be passed to the emmeans package for
visualisation of more complex models.modelPlots (base graphics) and
ggmodelPlots (ggplot2)modelPlots to allow for simplest case
gene ~ Time + (1 | ID)lmmSeq(). Improves speed of calculation
of type 2 Wald test. Overall speed increase of 25-50%.glmmSeq()id column name from the RE term
in the formula... option to fcPlot which is passed
to plotly() or ggplot()annotationPosition=FALSE in fcPlot so
arrows/connectors are not movedreturnList to return glmmSeq output as list
(to make error catching easier)glmmSeqThese 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.
Health stats visible at Monitor.