brms
stan
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brms | stan | |
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9 | 44 | |
1,224 | 2,504 | |
- | 0.8% | |
9.3 | 9.5 | |
6 days ago | 7 days ago | |
R | C++ | |
GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
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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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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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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.
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)
stan
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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?
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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
- Automatic differentiation in C
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[D] Programming language for developing computational statistics algorithms
I'd say take a look at Stan (https://mc-stan.org/)
Well it sounds a lot like you are listening to developers talk about coding languages they like for high performance compute. This is not what you want to be spending all your time doing afaik. The more appropriate languages to get into would be the classic Python and R. Julia if you dont give a shit about productionizing your code and https://mc-stan.org/ Stan if you are really locked into bayesian inference and wanted to pick julia anyway.
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Is python necessary to learn machine learning?
Even if RStudio & the Tidyverse have mostly been promoting a functional programming style in R, it has full support for OOP (see R6 or R7 for more modern implementations of it). Let's not even mention the excellent Stan ecosystem for Probabilistic programming / Bayesian modeling, or Bioconductor, the biggest repository of bioinformatics packages & tools of any language.
- [Q] Updated book or review paper on MCMC methods
- Stan in Nim?
What are some alternatives?
PyMC - Bayesian Modeling and Probabilistic Programming in Python
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
rstan - RStan, the R interface to Stan
Elo-MMR - Skill estimation systems for multiplayer competitions
probability - Probabilistic reasoning and statistical analysis in TensorFlow
tinytex - A lightweight, cross-platform, portable, and easy-to-maintain LaTeX distribution based on TeX Live
pyro - Deep universal probabilistic programming with Python and PyTorch
stat_rethinking_2020 - Statistical Rethinking Course Winter 2020/2021
MultiBUGS - Multi-core BUGS for fast Bayesian inference of large hierarchical models
fastbaps - A fast approximation to a Dirichlet Process Mixture model (DPM) for clustering genetic data
arviz - Exploratory analysis of Bayesian models with Python