brms
stan
brms  stan  

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brms

Bayesian Structural Equation Modeling using blavaan
[2] https://paulbuerkner.github.io/brms/

[Q] Correlated multivariate Beta model
Maybe something like the Logistic Normal ? (e.g. see this issue from brms). If that fits what you are looking for, you can use brms to generate the Stan code for you (brms::make_stan_code()) and work from that.

Stepbystep example of Bayesian ttest?
Okay so first off, I recommend that you read [this](https://link.springer.com/article/10.3758/s1342301612214) article about "The Bayesian New Statistics", which highlights estimation rather than hypothesis testing from a Bayesian perspective (see Fig. 1, second row, second column). Instead of a ttest, then, we can *estimate the difference* between two groups/variables. If you want to go deeper than JASP etc, I recommend that you use [brms](https://paulbuerkner.github.io/brms/), or, if you want to go even deeper, [Stan](https://mcstan.org/) (brms is a frontend to Stan).

[R] Are there methods for ridge and lasso regression that allow the introduction of weights to give more importance to some observations?
I think the brms package (https://github.com/paulbuerkner/brms) or the blavaan package (http://ecmerkle.github.io/blavaan/) have support for SEM. I've never done it myself, so I unfortunately can't give you any direction for that in particular. However, I have used stan in multilevel metaanalysis regression (combining multiple CRISPRa experiments to find determinants of CRISPRa activity, see https://github.com/timydaley/CRISPRasgRNAdeterminants/blob/master/metaAnalysis/NeuronAndSelfRenewalMetaMixtureRegression.Rmd) and had some success.

Package for :Generalized Mixed Effects Models for ZeroInflated Negative Binomial distributions ?
brms baby

Multiple observers
Could also be done using brms and the gr term. See this for the motivation behind this syntax.

I have a small sample size time series with potentially lagged predictor values which are also time series. What could be potential methods to analyse these data?
Anyway, I found I can include weights into the brm function by using gr(RE, by = var) to deal with the heterogeneous variance and it should automatically assume that each observation within a group is correlated according to the brms reference manual.

Brms: adding on a nonlinear component to working MLM model
This is what actually should work I must be declaring my variables incorrectly. The issue I'm having is that what you refer to as lin , I tried calling a few things, from b to LinPred (which worked in the link here: brms issue 47). When I've tried doing this, I receive errors that say "The following variables are missing from the dataset....[insert variable used to symbolize linear part of the model)". But I believe you're code is on the right path for what needs to be done I'll try altering my syntax to be sure it resembles yours let you know if it works.
stan
 Stan: Statistical modeling and highperformance statistical computation

Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
Alternatives: stan and edward
 How often do you see Bayesian Statistics or Stan in the DS world? Essential skill or a nice to have?

Rstan Package in ATPA
remove.packages(c("StanHeaders", "rstan")) install.packages("rstan", repos = c("https://mcstan.org/rpackages/", getOption("repos")))

[Q] Is there a method for adding random effects to an interval censored time to event model?
My approach to problems like this is to write down the proposed model mathematically first, in extreme detail. I find hierarchical form to be the easiest way to break it down piece by piece. Once I have the maths then I turn it into a Stan model. Last step is to use the Stan output to answer the research questions.

HELP Conjugate Priors in Bayesian Regression in SPSS
Here is a good breakdown of recommendations from Andrew Gelman.

Demand Planning
For instance my first choice in these cases is always a Bayesian inference tool like Stan. In my experience as someone who’s more of a programmer than mathematician/statistician, Bayesian tools like this make it much easier to not accidentally fool yourself with assumptions, and they can be pretty good at catching statistical mistakes.

What do actual ML engineers think of ChatGPT?
I tend to be most impressed by tools and libraries. The stuff that has most impressed me in my time in ML is stuff like pytorch and Stan, tools that allow expression of a wide variety of statistical (and ML, DL models, if you believe there's a distinction) models and inference from those models. These are the things that have had the largest effect in my own work, not in the sense of just using these tools, but learning from their design and emulating what makes them successful.
 ChatGPT4 writes Stan code so I don’t have to

How to get started learning modern AI?
oh its certainly used in practice. you should look into frameworks like Stan[1] and pyro[2]. i think bayesian models are seen as more explainable so they will be used in industries that value that sort of thing
[1] https://mcstan.org/
What are some alternatives?
rstan  RStan, the R interface to Stan
PyMC  Bayesian Modeling and Probabilistic Programming in Python
tinytex  A lightweight, crossplatform, portable, and easytomaintain LaTeX distribution based on TeX Live
jax  Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
bambi  BAyesian ModelBuilding Interface (Bambi) in Python.
stat_rethinking_2020  Statistical Rethinking Course Winter 2020/2021
probability  Probabilistic reasoning and statistical analysis in TensorFlow
rBAPS  R implementation of the BAPS software for Bayesian Analysis of Population Structure
EloMMR  Skill estimation systems for multiplayer competitions
testsaslinear  Common statistical tests are linear models (or: how to teach stats)
pyro  Deep universal probabilistic programming with Python and PyTorch