variance_jackknife() and
variance_bootstrap() to prevent estimation recycling,
results from those functions are now comparable to doing jackknife /
bootstrap “by hand”.predict() function for PLNfit model
to (i) return fitted values if newdata is missing or (ii) perform one VE
step to improve fit if responses are provided (fix issue #114)scale argument compute_offset() to force the
offsets (RLE, CSS, GMPR, Wrench) to be on the same scale as the counts,
like TSS.clusters) is not of the form 1:K_maxPLNLDA() and
changing extract_model() to conform with
model.frame()$VEStep() for PLN-PCA, dealing
with low rank matrices$project() for PLN-PCA, used to
project newdata into PCA space$latent_pos() which is equivalent to
active binding $latentNEWS.md file to track changes to the
package.
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