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It uses estimates of incidences of the common diseases occurring annually in the country.
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This statistic is an estimator of Nei's d XY statistic [ 40], though unlike d XY it uses estimated allele frequencies rather than allele counts, making it suitable for pooled sequence data.
It used estimates of the incidence of specific diseases and of the number of people exposed to second-hand smoke in particular areas.
For the constrained model, it uses the estimates from the free model for all the genes, fits the postulated relationship to the mean and re-estimates the dispersions.
First, it uses time estimates based on input from experienced diabetes providers, which might be substantially different with another panel of experts or of primary care providers (PCPs).
First, it uses an estimating equations approach to estimate mean scores.
It uses this estimated correlation to re-estimate the regression parameters and to calculate standard errors.
With regard to the latter, I think that it is possible to use estimates of actual intron loss rates from: Carmel et al. Genome Res.
It is instead common to use estimates calculated from every tenth intracranial area (ICA) [5, 6, 7, 8].
It is mentioned that previously used estimates across the phylogeny are used.
Thus, it would be desirable to use estimates of accuracy that the algorithm provides.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com