rstan
vroom
| rstan | vroom | |
|---|---|---|
| 8 | 3 | |
| 1,008 | 608 | |
| 1.7% | 0.8% | |
| 7.7 | 7.6 | |
| 13 days ago | 3 months ago | |
| R | C++ | |
| - | 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)
vroom
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Read in from a CSV only those lines which meet a certain condition?
Try Vroom
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Is there a way to load a large SAS7BDAT dataset into R efficiently with fair speed?
You can use an `rds` file. You have to read it in then write it out though. If you care about speed, then just use `readr::write_rds`, which is similar to the base `saveRDS`, but with compression off, but the file size will be much larger. You can also use random access objects, such as `fst`: https://www.fstpackage.org/, but again, need to write it out. I tried a quick benchmark and `haven` is much faster than `sas7bdat` package. If it's in a plain text delimited file, you can also look into `vroom`: https://github.com/r-lib/vroom
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what is the difference between read_csv and read.csv other than the speed ?
If you are looking for speed, I’d thoroughly recommend vroom: https://github.com/r-lib/vroom
What are some alternatives?
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
Rapidcsv - C++ CSV parser library
MultiBUGS - Multi-core BUGS for fast Bayesian inference of large hierarchical models
csvdecoder - Go library for parsing and deserialising CSV files into Go objects
paramonte - ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.
Vince's CSV Parser - A modern C++ library for reading, writing, and analyzing CSV (and similar) files.
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.
wasmr - Execute WebAssembly from R using wasmer
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.
csv
stanc3 - The Stan transpiler (from Stan to C++ and beyond).
miller - Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON