EJ
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 EJ
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I'll pass this on, but our GitHub issue list is alive and well... E.J.

Ah, I'm not sure, but I'll pass this on E.J.

Dear Agata, We are working to make the R code more easily accessible, and we are also completing a tutorial paper. The JASP functionality is based entirely on the BAS R package by Merlise Clyde! Cheers, E.J.

This is just a wild guess, but I think that BEST uses a tlikelihood, not a normal likelihood. This *may* explain the difference

Hi Sarah ANOVA is the same as linear regression when "treatment" (or whatever condition label you have) is a discrete factor. But the linear regression routine does need to realize that "treatment" is factor, not a continuous nu…

We don't have Bayesian polynomial contrasts in there just yet, but it is on our list. You can push us to do this by posting the issue on our GitHub page (for details see https://jaspstats.org/2018/03/29/requestfeaturereportbugjasp/). E.J.

Hi Kevin, Well, we don't have that in there just yet (although I like the idea of having a measure of association that is very general) but I guess most people would use linear regression and use polynomial predictors...so include both x, x^2 as pr…

Not yet! And I would definitely turn to time series analysis. The Fourier spectrum should be particularly helpful, I think. E.J.

Your calculator takes log to mean log10 (base 10), whereas we use "log" in R, which means "ln" (the natural logarithm with base e). Cheers, E.J.

I've forwarded this to our team. If you'd like to follow this up it would be best to post the issue on our GitHub page (for details see https://jaspstats.org/2018/03/29/requestfeaturereportbugjasp/). Cheers, E.J.

BF01 is just 1/BF10, so the same information is presented

If your conclusion depends crucially on whether you use Cohens's d or Hedge's g, this suggests that considerable caution is in order. But JASP provides both (I just saw we had a fix in our Hedges g  a new JASP version comes out end up this month a…

Posted on GitHub and being worked on / implemented, I believe E.J.

Looks good. I'd be consistent and use "Bayes factors" instead of "Bayes Factors". E.J.

I'll also ask that this will be made clear in the output or the help file. E.J.

I think you've since reported this on our GitHub page and we're implementing it... E.J.

Also, when in doubt, you can compare output from R to that of JASP. Cheers, E.J.

There's a 2009 paper in Psychonomic Bulletin & Review by Rouder & Morey: @ARTICLE{RouderEtAl2009Ttest, AUTHOR = {Rouder, J. N. and Speckman, P. L. and Sun, D. and Morey, R. D. and Iverson, G.}, TITLE = {{B}ayesian $t$ Tests for Accep…

Hi Joe, population effect size delta = population mu / population sd. So it's the population equivalent of Cohen's d (which is a summary based on the sample, not an inference) Cheers, E.J.

wrt 2: "likelihood" usually refers to something proportional to p(ytheta); so it has a technical connotation that does not match this particular context. wrt 3: Correct, with estimation there isn't a pair of hypotheses  there is only th…

Looks very nice! I love the idea of the annotations in general. Will pass this on to the lab as a recommended way to include explanations in tutorial papers. A few suggestions for improvement: I guess you could say that the posterior under H1 is &qu…

I'd like to add that, although it is customary to center the prior distribution around the testvalue, this is by no means required. In JASP, the ttests, binomial, multinomial (next release), and AB test (next release) all allow the location of the…

Hi Chris, That's exactly right. This is called the SavageDickey density ratio, and it is a convenient way to "see" a Bayes factor for H0 vs H1 without needing to compute the prior predictive adequacy for H0 and H1 separately. See for inst…

yes yes Not sure; what I see from the table is that "Size" is the only factor for which the data offer support (and they do so in compelling fashion) With many factors and interactions, the inclusion ("analysis of effects") appro…

If you are interested in the interaction, you can also compare the 2main effect model against the full model. With few models you don't always need the inclusion BF. Cheers, E.J.

Dear August, The ANOVA as borrowed from the BayesFactor package is really a linear mixed model. But in general, the same assumptions apply as they do for the frequentist version. Yes, the Bayesian approach generally allows all sorts of more flexible…

What needs to be normal is the distribution of variances across subjects, right? I don't see why that wouldn't be (approximately) normal. In your RM ANOVA, I am not sure how your ultimate test involves the variance across the x,y,z. The interaction …

Hi Richard, So far, as you indicate, JASP has focused mostly on the *evidence*, that is, the relative predictive performance for two competing hypotheses (aka the Bayes factor). The reason that we have not put in prior for the hypotheses is that the…

I have attended the relevant JASP team members to this forum entry...

I've passed this on...