marcle

I always thought that the type-safe XML/HTML in Ur/Web was elegant.

For examples, see http://www.impredicative.com/ur/demo/

Tobacco control advocates sometimes referred to a "scream test": the more vigorously the industry opposed a measure, the more likely that the measure was effective.

Answering my own question: Umbra and PostgreSQL include "#" as an operator for bitwise xor, whereas DuckDB does not.

This is very interesting code that uses Umbra v0.2-957, PostgreSQL 17.2, and DuckDB v1.1.3.

One minor point: some of the problems could not be implemented in DuckDB due to a missing xor operator - but the documentation includes an xor function (https://duckdb.org/docs/sql/functions/numeric.html#xorx-y) for the same version. Is the form of the function not suitable?

Nice childhood memories: "Thunderbirds are go!"

I always wondered how the palm trees were able to be flattened:).

This is a very interesting IDE to support data science.

Has anyone tried using the Eclipse Theia IDE for data science?

One of these IDEs may finally entice me away from ESS, magit and Org Mode.

"PSA" can also be probabilistic sensitivity analysis. I do health economic evaluations for prostate cancer screening -- which leads to interesting ambiguities:).

The article references the SSJ Java package which is developed by the author and his colleagues. This provides implementations for many RNGs -- and some nice tools for simulations: https://github.com/umontreal-simul/ssj

And see the discussion at https://discuss.ocaml.org/t/owl-project-concluding/14117

See also some discussion at https://www.reddit.com/r/ocaml/comments/1au329z/owl_project_...

ErgoAI is as "an enterprise-level extension of the Flora-2 system" which was recently open-sourced: https://github.com/ErgoAI . It seems to be well documented.

See https://fricas.github.io/, which includes a 990 page book and detailed documentation.

This is a nicely balanced discussion of the two languages - with much food for thought.

I wrote a purely functional AD library in Mercury [0], which adapts a general approach from [1]. I believe that Owl provides a similar approach [2].

[0] https://github.com/mclements/mercury-ad

[1] https://github.com/qobi/AD-Rosetta-Stone/

[2] https://github.com/owlbarn/owl/tree/main/src/base/algodiff

The following homage to Aphyr’s "Typing the technical interview" solves the N-queens problem using types in Typescript. This is both funny and insightful.

https://www.richard-towers.com/2023/03/11/typescripting-the-...

Waldek Hebisch, who is the maintainer for the Fricas computer algebra software, also maintains a 64-bit version of Poplog for amd64/x86_64:

https://github.com/hebisch/poplog https://www.cs.bham.ac.uk/research/projects/poplog/freepoplo...

This is a free book on Owl. I have only looked at the chapter on automatic differentiation - and it looks good.

I find the solutions from https://github.com/qobi/AD-Rosetta-Stone/ to be very helpful, particularly for representing forward and backward mode automatic differentiation using a functional approach.

I used this code as inspiration for a functional-only (without references/pointers) in Mercury: https://github.com/mclements/mercury-ad

My go-to projects have varied over time:

1. An implementation of Fisher's exact test for 2x2 tables. This adapts the algorithm from R's fisher.test() function, which was implemented with function closures for optimisation, which does not always map well to other languages.

2. A foreign function interface to the Rmath library. I have now done this for Standard ML (MLton, Poly/ML, Moscow ML, MLkit, SML/NJ, SML# and Manticore), OCaml, Ur/Web, Mercury and MonetDB. Code generation sometimes uses cpp, m4 or the language's tree and string facilities.

3. Discrete event simulation using message passing with an application to cost-effectiveness analysis. Someday, I'll get this published.

Another relevant software category is the statistical analysis languages, including SAS, Stata and SPSS.

Old-school SAS included only two data types (floats and character strings), but allowed for SQL and sequential data-steps to live together. Persistence was baked in. The floats could be used to represent dates, datetimes and other formats. I particularly appreciated being able to use macros to define a data-step view to split the follow-up for an individual from a table. Such a view could then be collapsed using SQL. More recently, R tools such as dplyr have brought together data-frames and relational operations. However, I miss the sequential coding in SAS, using macros as higher-level tools to define the logic, including corner cases.

For strictly typed records, I have always wanted to spend more time with SML# [0] this allows for record updating, with close ties to SQL -- an under-appreciated version of SML.

[0] https://github.com/smlsharp/smlsharp

This is a very interesting article.

The author notes: "There is a compiler for KL1 to C available [2], which seems to work, but suffers from bit rot." Note that Ueda and colleagues have been updating this software (KLIC): see https://www.ueda.info.waseda.ac.jp/software.html for details. Ueda's work on LMNtal is also very interesting.

TLDR: Arrow and DuckDB provide fast database aggregates compared with R's RDS format and, to an extent, SQLite.

It is unclear how much functionality is available for Arrow under R: any comments? It would also be interesting to see a similar benchmark for Python, which could include the embedded version of MonetDB -- an R package for MonetDB/e is not yet available.

Edit: amended the TLDR to reflect jhoechtl's and wodenokoto's comments. SQLite provided reasonably memory efficient aggregates.

+1. I was about to post an almost identical comment.