ParBayesianOptimization
rstan
| ParBayesianOptimization | rstan | |
|---|---|---|
| 1 | 8 | |
| 99 | 1,009 | |
| - | 1.2% | |
| 0.0 | 7.7 | |
| over 1 year ago | 17 days ago | |
| R | R | |
| - | - |
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ParBayesianOptimization
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[D] Selecting Hyperparameters Using Bayesian Optimization
Disclaimer: I am the maintainer of ParBayesianOptimization. That readme has a pretty good walkthrough of how Bayesian optimization works.
rstan
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R packages in Colab - either speed up install, or import library?
I have a Colab notebook with an R kernel that I'm using to share with students for remote lessons in statistics. This notebook relies on "rstanarm", which is pretty massive with the number of dependencies - it takes ~50minutes to install into a fresh Colab session with install.packages(). It seems the issue is that many of the dependencies of this package need to be compiled from source, which takes a long time on Linux distributions like Colab.
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Help troubleshooting a an error in a brms Regression
You need to install the preview version of rstan: https://github.com/stan-dev/rstan/wiki/Configuring-C---Toolchain-for-Windows
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Time series tutorial series
If you're on Windows, there are extra hoop to jump through, I'm afraid https://github.com/stan-dev/rstan/wiki/
- [S] Pyro/Numpyro or Stan for Bayesian modeling?
- Why does rstan depend on V8?
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Help with error running stan model using brms package
And here are the instructions on how to build RStan from source: https://github.com/stan-dev/rstan/wiki
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trouble installing rstan on mac
I ran the R code from here
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Looking to do Bayesian two-way ANOVA - can someone point me in the right direction?
In R, the rstanarm package should do you well. You'll need to install rstan and make sure you have a C++ complier set up as well (instructions here: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started)
What are some alternatives?
tmle3mopttx - 🎯 💯 Targeted Learning and Variable Importance for the Causal Effect of an Optimal Individualized Treatment Intervention
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
vip - Variable Importance Plots (VIPs)
MultiBUGS - Multi-core BUGS for fast Bayesian inference of large hierarchical models
mlr3learners - Recommended learners for mlr3
paramonte - ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.
ggplot2 - An implementation of the Grammar of Graphics in 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.
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
vroom - Fast reading of delimited files
stanc3 - The Stan transpiler (from Stan to C++ and beyond).