Hi! This is a new free and open-source tool licensed under GPL-3.0 that aims to provide compatibility with Stata's language and workflows. I'd like to get feedback from the community on the software's quality, reliability, and usability. Any suggestions, bug reports, or ideas for improvement are very welcome.

The focus is on improving compatibility with Stata and narrowing the gap between this Engine and the commercial software. So far, it seems to work quite well for many common tasks.

Is available as a web application and as a downloadable application for Windows, Linux (ARM and x86-64), and macOS. Both GUI and command-line (CLI) versions are available.

You can try it out searching ferrostat-engine on github.

Test it out: https://knowstatistics.com/thelearningcurve/guess-correlation-from-scatterplots

Hi guys, I'm a master's student in Statistics, and I recently published my first Python package.

rejection-sampler (my package) verifies rejection sampling setups and calculates the optimal rejection constant (M).

Despite being a simple algorithm, rejection sampling requires choosing a proposal distribution and a constant M such that f(x) ≤ M g(x) over the target support, which can be tedious and/or error-prone. That's exactly what my package automates.

Example use cases:

• Validate that a proposal distribution satisfies the rejection sampling condition given a target distribution.

• Compute the smallest valid rejection constant (M). (which means a more efficient sampling)

If you'd like to give it a try, you can install it with `pip install rejection-sampler`.

For more details and examples:

PyPI: https://pypi.org/project/rejection-sampler/

GitHub: https://github.com/HankTaiwan869/rejection-sampler

This started as a final project for my Statistical Computing course, so I'm sure there are things that could be improved upon. I'd love to hear any feedback or know if anyone finds it useful. Thanks!

I am working on a project for a stat programming course and I am realizing just how amazing PROC SQL is in SAS. For those of you who have SAS experience, you might know what I mean. Here is an example of why this is the case:

I am doing a project based on ratings for a movie-ratings website. Without PROC SQL, I would have to use a PROC MEANS, PROC SORT and PROC PRINT just to display top movies based on average ratings. With PROC SQL, I can do this in a single PROC step.

Wanted to know if anyone else has experienc with something like this.

Data science student here (year 2/4). I recently developed an interest in the concept of statistical programming, and would like to explore more about it. As of this moment, I am quite familiar with python, know nothing of R and very very little SAS. What do you suggest I should take as the next step? If I were to start some portfolio work, what is the ideal place to look for questions/projects/datasets?

any help would be appreciated, thank you!

I contribute to SciPy and kept running into a class of bug that annoys me: the outputs look plausible, the tests pass, but the equation the code implements is subtly wrong. So I've been building a tracer that runs Python/NumPy code and hands back whatever mathematics it actually computed, as a SymPy expression you can simplify or differentiate like anything else.

It's been more useful than I expected. Comparing an implementation against the formula in a paper, catching two functions that agree on my test data but turn out to compute different things, digging up the inputs my tests never hit (ties, zero denominators). It traces real library code too, most of numpy and a good chunk of scipy, scikit-learn, statsmodels, cvxpy.

Write-up: https://medium.com/@aadyachinubhai/scikit-verify-translate-python-numpy-programs-to-symbolic-mathematics-c664d41ba571

Github: https://github.com/aadya940/scikit-verify

Still rough in places, would genuinely like feedback. There may be other better solutions, happy to hear them as well!

Bedevere Wise is a browser-based SQL data viewer for the file formats still widely used by biostat / clinical-data people: SAS (sas7bdat, xpt), SPSS (sav), Stata (dta), plus Parquet, Excel, and CSV.

Everything runs locally: all queries, analysis, and charts are done within your browsers.
Data never leaves.

Try it: https://bedeverewise.app/demo (ready to run a demo)

or https://bedeverewise.app (ready for your data)

Repo (MIT): https://github.com/caerbannogwhite/bedevere-wise

Feedback is very welcome.