rstan
paramonte
rstan | paramonte | |
---|---|---|
8 | 4 | |
1,008 | 236 | |
1.7% | 5.1% | |
7.7 | 8.7 | |
12 days ago | 10 days ago | |
R | Fortran | |
- | GNU General Public License v3.0 or later |
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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)
paramonte
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Is fortran used at all anymore, or is it like driving around a model T car? I've got some programs written in fortran.
The ParaMonte Machine Learning library is an actively developed package in Fortran 2018 standard. The next release of the package contains about a million lines of Fortran (along with other languages). There are many more Fortran libraries, mostly in the Aerospace, Geology, Astronomy, Civil Engineering, and Petroleum industry and academia. Many electronic structure, nuclear, and plasma physics packages have been and are still developed in Fortran. Ask this question on the Fortran Community Discourse to get a more comprehensive list of current Fortran packages.
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Do any of you do modeling with pymc3 or other Bayesian-oriented packages?
Bayesian modeling is at the heart of scientific inference and uncertainty quantification. Whether the industry uses it or not, does not devalue this important approach. If they do not then it is likely that they have not yet realized its significance. But I suspect many do, in collaboration with Academia and they typically use their own specialized high-performance tools for such inferences since their models are far more complex than things that could be implemented via such high-level probabilistic programming languages as pymc3. Incidentally, our lab has developed (and is still developing) a High-Performance serial/parallel package for sampling and integration of Bayesian posteriors which is available from multiple programming languages including C/C++/Fortran/Python/MATLAB/...: https://github.com/cdslaborg/paramonte
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Can I use a function or procedure as input of a subroutine in fortran?
https://github.com/cdslaborg/paramonte/blob/e3087ef9c9b13c53c5298e4abea2bcb5043ab8af/src/kernel/Integration_mod.f90#L108
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Fit data as you like
Can you provide more information about your data? How many dimensions? 1D? Also, could you elaborate on what you mean by fitting a Gaussian to the time series data? Do you mean a Gaussian process? My lab has written a fast generic Bayesian optimizer and sampler library, in pure modern Fortran, that can not only find the best-fit parameters of your time-series model (whether polynomial, sin, ...), but can also put constraints on the uncertainties associated with the parameters. Writing a generic likelihood function for polynomial or other types of fits is quite easy. Once you write it, you simply compile and link it with this library to find the best-fit parameters of each model. The prebuilt ready-to-use versions of the library are also available on the GitHub release page.I would be happy to help you further with writing the polynomial/sin models and fitting them to your data with this library. But some further information is needed from your side to write the objective functions for different models (poly, sin, ...).
What are some alternatives?
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
ftl - The Fortran Template Library
MultiBUGS - Multi-core BUGS for fast Bayesian inference of large hierarchical models
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.
modAL - A modular active learning framework for Python
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.
climt - The official home of climt, a Python based climate modelling toolkit.
vroom - Fast reading of delimited files
Nerve - This is a basic implementation of a neural network for use in C and C++ programs. It is intended for use in applications that just happen to need a simple neural network and do not want to use needlessly complex neural network libraries.
stanc3 - The Stan transpiler (from Stan to C++ and beyond).
pretty-print-confusion-matrix - Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib