bambi
brms
bambi | brms | |
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5 | 9 | |
1,013 | 1,231 | |
0.9% | - | |
8.0 | 9.2 | |
5 days ago | 4 days ago | |
Python | R | |
MIT License | GNU General Public License v3.0 only |
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bambi
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Bayesian Structural Equation Modeling using blavaan
It is much less challenging with Bambi[1] and brms[2].
[1] https://bambinos.github.io/bambi/
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Ask HN: What Are You Learning?
I’m trying to learn statistics. I’m up to implementing regressions in python using sci-kit 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.
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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.
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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
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.
What are some alternatives?
deffcode - A cross-platform High-performance FFmpeg based Real-time Video Frames Decoder in Pure Python 🎞️⚡
rstan - RStan, the R interface to Stan
mistletoe - A fast, extensible and spec-compliant Markdown parser 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.
vimtk - A vim toolkit focused on gvim, IPython, and the terminal.
tinytex - A lightweight, cross-platform, portable, and easy-to-maintain 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
static-frame - Immutable and statically-typeable DataFrames with runtime type and data validation
tests-as-linear - Common statistical tests are linear models (or: how to teach stats)
auto-editor - Auto-Editor: Effort free video editing!
rBAPS - R implementation of the BAPS software for Bayesian Analysis of Population Structure