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Brms Alternatives
Similar projects and alternatives to brms

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brms reviews and mentions

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.

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.
Unfortunately, I can't just tag it onto to the working linear piece because brms doesn't allow for more than 2 level factor covariates in NL formulas. After much googling, I was able to find these brms github posts: 46 47 where they discuss how a NL component can be added. I've tried the syntax used, but it's still throwing errors. Here is one syntax I tried, going off of the information on those two links (where b1=lambda, b2= kappa)

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Stats
paulbuerkner/brms is an open source project licensed under GNU General Public License v3.0 only which is an OSI approved license.
The primary programming language of brms is R.