Contribution Ideas

Special Functions

The functions underlying the statistical distributions require skills in numerical programming. If you like being ‘close to the metal’, this is a good area for contributions. Suitable for medium-advanced level programmers. In particular we need implementations of:

• gamma
• incomplete gamma (upper & lower)
• inverse incomplete gamma

This work is partially complete and makes a good starting point for someone who wants to make a substantial contribution.

Documentation

Better and more documentation is always welcome, and a great way to learn. Suitable for beginners to Common Lisp or statistics.

Jupyter-Lab Integrations

Jupyter Lab has two nice integrations with Pandas, the Python version of Data-Frame, that would make great contributions: Qgrid, which allows editing a data frame in Jupyter Lab, and Jupyter DataTables. There are many more Pandas/Jupyter integrations, and any of them would be welcome additions to the Lisp-Stat ecosystem.

Regression

We have some code for ‘quick & dirty’ regressions and need a more robust DSL (Domain Specific Language). As a prototype, the -proto regression objects from XLISP-STAT would be both useful and be a good experiment to see what the final form should take. This is a relatively straightforward port, e.g. defproto -> defclass and defmeth -> defmethod. Skill level: medium in both Lisp and statistics, or willing to learn.

Vector Mathematics

We have code for vectorized versions of all Common Lisp functions, living in the nu package. It now only works on vectors. Shadowing Common Lisp mathematical operators is possible, and more natural. This task is to shadow the Common Lisp mathmatical operators, e.g. + so that they will work on atoms and vectors; essentially implement a superset that is ANSI compatible. This task requires at least medium-high level Lisp skills, since you will be working with both packages and shadowing. We also need to run the ANSI Common Lisp conformance tests on the results to ensure nothing gets broken in the process.

Continuous Integration

If you have experience with Github’s CI tools, a CI setup for Lisp-Stat would be a great help. This allows people making pull requests to immediately know if their patches break anything. Beginner level Lisp.

Quarto

Integrate Lisp-Stat with Quarto. If Python, R and Julia can integrate, so can Common Lisp. We already have common-lisp-jupyter which gets us pretty close. It’s mostly a Quarto configuration exercise and modifying plot/vega to output PNG files.