I now understand better why wiener waffeln exist.... every time my initial values are rejected I eat one
#rstats #brms #rstan #ddm

📰 Happy to announce the publication in @PeerCommunityJournal of our analysis of #VaginalMicrobiota dynamics.

🦠 This work provides unprecedented insights into the occurrence of #dysbioses.

Thanks to the #women participating in the #PAPCLEAR study at CHU Montpellier (France) 🙏

The model (all in #RStan) was built by @tsukushi_kamiya and the #16S sequencing of over 2.000 samples was done in Jacques Ravel's lab. This work stems from the @ERC_Research #EVOLPROOF project.

https://peercommunityjournal.org/articles/10.24072/pcjournal.527/

Heureux d'annoncer la publication dans @PeerCommunityJournal de notre analyse de la dynamique du #MicrobioteVaginal.

Ces travaux sont issus de l'étude #PAPCLEAR promue par le #CHU_Montpellier et financée par l' @ERC_Research, qui représente l'un des suivis les plus longs à ce jour.

Le magnifique modèle statistique (tout en #RStan) a été réalisé par @tsukushi_kamiya et le séquençage #16S de plus de 2000 échantillons réalisé dans le laboratoire de Jacques Ravel.

https://peercommunityjournal.org/articles/10.24072/pcjournal.527/

#statstab #181 Comparing Bayesian Approaches by @jebyrnes

Thoughts: Compares running models in #rethinking, #stan, #brms, #inla, and #glmmTMB via #TMBstan. It's nice to have options.

#bayes #bayesian #rstan #r #stats #rstats

https://biol609.github.io/lab/alt_to_rethinking.html

#statstab #169 {priorsense} prior diagnostics and sensitivity analysis

Thoughts: Bayesian modelling requires more scrutiny of how one's choices impact outcomes. This packages has handy functions + plots.

#brms #bayesian #rstan #priors
#mcmc #diagnostics

https://n-kall.github.io/priorsense/

Обзор библиотеки Stan в R

Приветствую! Stan - это библиотека на C++, предназначенная для байесовского моделирования и вывода. Она использует сэмплер NUTS, чтобы создавать апостериорные симуляции модели, основываясь на заданных пользователем моделях и данных. Так же Stan может использовать алгоритм оптимизации LBFGS для максимизации целевой функции, к примеру как логарифмическое правдоподобие . Для облегчения работы с Stan из языка программирования R доступен пакет rstan , который предоставляет интерфейс R для Stan. Сегодня мы и рассмотрим этот пакет.

https://habr.com/ru/companies/otus/articles/794196/

#rstan #r #аналитика #машинное_обучение #c++

If I am a toddler in my #statistics development, then I could be said to be a baby in #Rstats, but I haven't even had my umbilical cord cut in #bayes. I installed #rstan this morning, then ran the schools.stan example.

My laptop didn't seem to do anything so I assumed that I had installed incorrectly and so went to make a cup of tea.

Imagine my surprise when I returned to a screen full of all sorts of stuff.

I am the noobiest of noobs. A lot to learn!

Trying to work out the simplest way to find out if my team’s Windows permissions allow them to compile Stan models. Have assumed that compiling models is like compiling packages (is that a good assumption?)
I've come up with:
1. Install Rtools (cran.ma.imperial.ac.uk/bin/windows/Rtools)
2. > install.packages("devtools")
3. Find a small, intriguing package: BRRR
4. > devtools::install_github("brooke-watson/BRRR", build = TRUE)
#Rstats #R #Rstan

You've heard about Stan and want to learn some more? Maybe you're about to step into the Bayesian paradigm and don't know where to start 🤔

In this week's blog post, we'll take a look what you can do with #Stan and draw comparisons to #JAGS!

https://jumpingrivers.com/blog/why-stan/

#DataScience #RStats #Python #RStan #PyStan #Bayesian

Anyone know any good intro resources for #rstan that cover ADVI as well as (or instead of) NUTS?

My #Rstats package for creating interactive #bayesian visualizations is finally available on CRAN :https://ignacio82.github.io/vizdraws/
The package can probably use better documentation, additional features, and it may have some bugs. However, I thought that the best way to keep improving it was to push it.

#rstan #datascience

More support for Bayesian analysis in the sj!-packages #rstats #rstan #brms https://link.rweekly.org/6jg #rstats #datascience