observation deviates from what we would expect at the given timepoint. The coefficients beta[i] are equal across timepoints by design, so this does not equate them per se. Asparouhov et al. in the original DSEM paper are a bit vague about this, but I think what we do what they call latent centering.

Thanks! The term b[i]x[i,t-1] is there to catch up systematic variations with some time-varying predictor x[i,t-1]. We subtract it for the same reason as we subtract alpha_i, namely to get rid of systematic variation. Hence, what's left inside the parentheses is the residual, i.e., how much the

The No-U-Turn Sampler, utilizing Hamiltonian Monte Carlo, was very efficient for estimating these models, and the paper is accompanied by annotated Stan code which practitioners can modify to their needs.
Out in Multivariate Behavioral Research today, open access:
doi.org/10.1080/0027...

and then do some tiny changes that removed the issue and kept the package on CRAN. Morale of the story: CRAN rules are there for a reason, and it's possible to figure things out if you just try obsessively enough.
Now time for resubmission!

I think I can check all the boxes. Feel free to DM me if you still need this.

og dermed får mindre frihet til å disponere egen inntekt og det blir mindre penger å investere i teknologi som vi trenger for å kunne konkurrere med USA og Kina. Jeg er redd vi ikke er noe bedre enn resten av Europa, og det er synd. Jeg synes denne beskriver det bra: www.politico.eu/article/euro...

Jeg tror Norge lider av en forestilling om at velferdsstaten ikke bare skal være et sikkerhetsnett, men en forsikring som skal ordne alt, og at alt kan løses med større offentlig sektor. Strømstøtten og sykelønnsordning er gode eksempler. Prisen er høy, fordi alle må betale forsikringspremie (skatt)

it's almost a black box algorithm, self-tuning with minimal user input. Rankings remain tricky combinatorial objects so it's still quite computationally heavy, but we hope this is a step forward in making Mallows models more practically applicable.

arxiv.org/abs/2412.13644

so here we've instead developed nested sequential Monte Carlo algoriths, SMC^2 among friends. We derive the algorithms for a very general case of the Mallows model, and test them on complete rankings, top-3 rankings, pairwise preferences, and clustering. An additional advantage of SMC^2 is that

Thanks! I'll check it out.

Thanks!

Yes, I'd like to be added