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common-lisp-stat
Common Lisp Statistics -- based on LispStat (Tierney) but updated for Common Lisp and incorporating lessons from R (http://www.r-project.org/). See the google group for lisp stat / common lisp statistics for a mailing list.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Measurements.jl
Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration.
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On a quick scour of the source code at https://github.com/Lisp-Stat/lisp-stat, I can see that there's a `Copyright (c) 1991 by Luke Tierney` on `base/variables.lisp` in the initial commit. Interestingly, the code is released under the Microsoft Public License, which includes the text: "Copyright Grant- Subject to the terms of this license, including the license conditions and limitations in section 3, each contributor grants you a non-exclusive, worldwide, royalty-free copyright license to reproduce its contribution, prepare derivative works of its contribution, and distribute its contribution or any derivative works that you create" which would imply that the answer to the GP's question needs to be "yes".
Note: I have no idea who Luke Tierney is or what his contributions to this area might be, which is a failing on my part.
Right, from my understanding, Lisp-Stat originated with Luke Tierney.
The latest efforts to extend and work with those ideas/code base are at https://github.com/blindglobe/common-lisp-stat and work by Tamas Papp (https://tamaspapp.eu/post/orphaned-lisp-libraries/)
Forking open source code is fine, but why try to take over the name?
Aside from the language features, some of the libraries in Julia make it really useful for statistical computing. One really cool library I am trying to use more and more in Julia is the Measurements library [1]. With the multiple dispatch system in Julia its super easy to integrate into most problems and can let you estimate error bounds on values programs produce. Super important for scientific applications.
I am hoping in the future that I can mix this in with some auto-diff problems to get uncertainty bounds on estimation problems with minimal fiddling with covariance matrices. Right now the performance is the only problem in integrating the library into pretty much any problem :(
[1] https://github.com/JuliaPhysics/Measurements.jl