vroom
Fast reading of delimited files (by tidyverse)
rstan
RStan, the R interface to Stan (by stan-dev)
| vroom | rstan | |
|---|---|---|
| 3 | 8 | |
| 608 | 1,009 | |
| 0.2% | 1.2% | |
| 7.6 | 7.7 | |
| 3 months ago | 17 days ago | |
| C++ | R | |
| GNU General Public License v3.0 or later | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
vroom
Posts with mentions or reviews of vroom.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-03-02.
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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
rstan
Posts with mentions or reviews of rstan.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-27.
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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?
When comparing vroom and rstan you can also consider the following projects:
Rapidcsv - C++ CSV parser library
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan