brms
rBAPS
brms | rBAPS | |
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9 | 2 | |
1,276 | 10 | |
- | - | |
9.2 | 8.5 | |
7 days ago | 19 days ago | |
R | R | |
GNU General Public License v3.0 only | GNU General Public License v3.0 only |
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brms
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Bayesian Structural Equation Modeling using blavaan
[2] https://paul-buerkner.github.io/brms/
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[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.
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Step-by-step example of Bayesian t-test?
Okay so first off, I recommend that you read [this](https://link.springer.com/article/10.3758/s13423-016-1221-4) 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 t-test, 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://paul-buerkner.github.io/brms/), or, if you want to go even deeper, [Stan](https://mc-stan.org/) (brms is a front-end to Stan).
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[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/paul-buerkner/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 multi-level meta-analysis regression (combining multiple CRISPRa experiments to find determinants of CRISPRa activity, see https://github.com/timydaley/CRISPRa-sgRNA-determinants/blob/master/metaAnalysis/NeuronAndSelfRenewalMetaMixtureRegression.Rmd) and had some success.
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Package for :Generalized Mixed Effects Models for Zero-Inflated Negative Binomial distributions ?
brms baby
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Multiple observers
Could also be done using brms and the gr term. See this for the motivation behind this syntax.
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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.
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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.
rBAPS
- [Help] How do I get BAPS program if it isn't available online?
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I need heeelp for my thesis
There is an R implentation and another that seems to implement it slightly differently
What are some alternatives?
rstan - RStan, the R interface to Stan
rmarkdown - Dynamic Documents for R
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.
metaflow - Open Source Platform for developing, scaling and deploying serious ML, AI, and data science systems
tinytex - A lightweight, cross-platform, portable, and easy-to-maintain LaTeX distribution based on TeX Live
bambi - BAyesian Model-Building Interface (Bambi) in Python.
stat_rethinking_2020 - Statistical Rethinking Course Winter 2020/2021
tests-as-linear - Common statistical tests are linear models (or: how to teach stats)
CRISPRa-sgRNA-determinants
bayesian - Bindings for Bayesian TidyModels