brms
bambi
brms  bambi  

9  5  
1,280  1,075  
  0.7%  
9.3  7.3  
12 days ago  about 2 months ago  
R  Python  
GNU General Public License v3.0 only  MIT License 
Stars  the number of stars that a project has on GitHub. Growth  month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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.
bambi

Bayesian Structural Equation Modeling using blavaan
It is much less challenging with Bambi[1] and brms[2].
[1] https://bambinos.github.io/bambi/

Ask HN: What Are You Learning?
I’m trying to learn statistics. I’m up to implementing regressions in python using scikit learn.
I was playing around with Bayesian modelling last night with https://bambinos.github.io/bambi/ But I’m not really sure how to interpret the outputs.
Always open to reading about learning resources/books/videos/courses from others.

how can I build a regression model which is penalised for moving away from an assumed set of coefficients?
I would suggest using Python's bambi; it is based on PyMC and it is very straightforward to use. We simply define our priors argument as a dictionary (quite literally: my_priors = {"feature_1": bmb.Prior("Normal", mu=4, sigma=4), "feature_n": bmb.Prior("Normal", mu=0.4, sigma=0.4)}) when creating our Bambi Model object and we are ready to go. They have a lot of worked exampling in their website.

Which not so well known Python packages do you like to use on a regular basis and why?
For those interested in Bayesian modeling in Python we also have Bambi https://github.com/bambinos/bambi
 Release Bambi 0.6.0 · bambinos/bambi
What are some alternatives?
rstan  RStan, the R interface to Stan
deffcode  A crossplatform Highperformance FFmpeg based Realtime Video Frames Decoder in Pure Python 🎞️⚡
stan  Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
mistletoe  A fast, extensible and speccompliant Markdown parser in pure Python.
tinytex  A lightweight, crossplatform, portable, and easytomaintain LaTeX distribution based on TeX Live
pyroute2  Python Netlink and PF_ROUTE library — network configuration and monitoring
stat_rethinking_2020  Statistical Rethinking Course Winter 2020/2021
staticframe  Immutable and staticallytypeable DataFrames with runtime type and data validation
rBAPS  R implementation of the BAPS software for Bayesian Analysis of Population Structure
vimtk  A vim toolkit focused on gvim, IPython, and the terminal.
testsaslinear  Common statistical tests are linear models (or: how to teach stats)
autoeditor  AutoEditor: Efficient media analysis and rendering