There isn’t a good, maintained wrapper for SQLite that doesn’t have a restricted license. Using CFFI and autowrap, create a lisp interface for SQLite. This will allow us to use sqldf with something other than PostgreSQL.
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:
- 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.
Better and more documentation is always welcome, and a great way to learn. Suitable for beginners to Common Lisp or statistics.
Jupyter Lab has two nice integrations into 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.
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.
We have code for vectorized versions of all Common Lisp functions,
living in the
elmt package. It now only works on vectors. Shadowing
Common Lisp mathematical operators is possible, and more natural. This
task is to make
elmt vectorized math functions work on lists as well
as vectors, and to implement shadowing of Common Lisp. 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.
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.
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