Lisp-Stat Blog

• News About Lisp-Stat
• 2023 End of Year Summary
• Introduction to the Practice of Statistics
• Statistical Analysis with Lisp-Stat
• New Releases
• 2022 Release

News About Lisp-Stat

2023 End of Year Summary

We started the year by working through the examples from the first chapter of the Introduction to the Practice of Statistics. Whilst this is an excellent test for Lisp-Stat, Chapter 2 presented some major challanges to the plotting system, so the highlight for this year is Plot 2.0. This is a breaking change, however the fix for plots is relatively simple.

The updates for 2023 include:

• IPS Chapter 1 complete
• IPS Chapter 2 plotting
• Sampling data frames
• Adopted cl-gists so we can work with the Vega eco-system on Github
• Added Lisp Linear Algebra, CFFI wrappers for BLAS and LAPACK
• Improved tutorials, examples and documentation
• Plot 2.0
  • Smoothers for non-parametric regression
  • Plot helpers for qq-plots and scatterplots
  • New data sources: named data sets, URLs, embedded
  • Publish plots to Github gists
  • Edit plots in Vega Edit online editor
  • Fully support multi-layer, multi-data set plotting

Introduction to the Practice of Statistics

The examples from the first chapter of the book are now completed. This chapter focuses mostly on graphics, and the examples here supplement those in the plotting examples.

Statistical Analysis with Lisp-Stat

Lisp-Stat is the culmination of many months work to pull together the best-in-class statistical analysis packages available in Common Lisp, under a commercially friendly license, usable ‘out of the box’. It is an environment for statistical computing, conceptually similar to R, that is also suitable for front-line production deployments. It grew out of a desire to have an environment for rapidly prototyping analytical and A.I. solutions, and move directly to production environments with minimal friction. Typically there is a gap between the modeling environment, perhaps R or Python, and the delivery environment. Lisp-Stat closes that gap.

Lisp-Stat is in daily use for statistical analysis and is now considered ready for a broader audience.

Why Lisp?

We had a few requirements when evaluating options. Specifically the system had to:

• Work well in the kind of exploratory environment conducive to analytics and AI
• Be robust enough to be deployed in an enterprise-level production environment
• Be available under a license without source code restrictions

Common Lisp was the only framework that met all these requirements. And, it is not the first time lisp has been used in a statistical setting. XLISP-STAT, our spiritual predecessor, was a contemporary of R in the early days of development. Wikipedia says about it: “XLispStat was historically influential in the field of statistical visualization” and its author, Luke Tierney, was a member of the original R core team.

What does Lisp-Stat do?

Lisp-Stat provides support for vectorized mathematical operations, a super-set of common lisp array operations, and a comprehensive set of statistical methods that are implemented using the latest numerical algorithms. In addition, Common Lisp provides a dynamic programming environment that includes a REPL for interactive evaluation of code, CLOS, an excellent object oriented environment and a meta-object protocol (MOP).

We have also compiled an archive of XLISP-STAT libraries that can be ported with the aid of a compatibility package (XLS). This gives Lisp-Stat a leg up on ecosystem development; there are many useful statistical routines in the archive.

Getting Started

If you are familiar with R, and want to understand how to do similar things with Lisp-Stat, the basic tutorial is the place to begin. If you have a Common Lisp installation and want give Lisp-Stat a quick try, start with Install to Plot in 5 minutes. For newcomers to lisp, see the Installation section for how to get going from ground zero.

What’s in the box?

All the basics for descriptive statistical analysis.

Data Frames

Load the classic R mtcars data set into a data frame:

(data :mtcars)

Plotting

The plotting examples show how to plot the horsepower vs. miles-per-gallon of the similar Vega-Lite data set:

(plot:plot
  (vega:defplot hp-mpg
  `(:title "Horsepower vs. MPG"
    :data (:values ,vgcars)
    :mark :point
	:encoding (:x (:field :horsepower :type :quantitative)
	           :y (:field :miles-per-gallon :type :quantitative)))))

Jupyter notebooks

For reproducible research and sharing.

Examples & Tutorials

The examples show how to create commonly used statistical plots and the analytic examples work through the examples of the book Introduction to the Practice of Statistics using Lisp-Stat.

Example data sets

About 1500 R data sets can be loaded using the rdata package.

Where do I get help?

The community page page describes how the project is structured. Help can be obtained from:

• Lisp-Stat mailing list
• Stack overflow
• Github discussion

Where did it come from?

Several of these libraries are based on the work of Tamas K Papp, who was working as a practicing econometrician using Common Lisp before leaving the community for Julia. One of the design goals for Lisp-Stat was to avoid the Lisp Curse that plagues so many common lisp niches, including statistics. So, rather than reinvent the wheel, we took some existing wheels, greased and polished them, added documentation and filed off the rough edges to make them work smoothly together in an integrated environment. The greenfield work includes special functions and plot.

Haven’t I seen this before?

You may have. In late March 2021 existence of Lisp-Stat was leaked to Hacker News and Reddit. At that time, we were still getting Lisp-Stat ready for general release, and things were not quite where we wanted them to be. If you saw the original leaked version, here are the changes:

• Documentation:
  • Reorganized getting started, tutorials, reference
  • Added plotting and data-frame examples
  • Added “Install to plot in 5 minutes” getting started guide
  • Expanded analytic examples
  • Expanded basic tutorial
  • QA: spelling, grammar, typos, examples
  • Added resources section
  • Added search
• Data Frame
  • Added pretty printing
  • Added markdown printing
  • Added variable shortcuts, you can now refer to a variable by name in all contexts
  • Added head, tail, summary, column-name functions
  • Added define-data-frame macro
  • Miscellaneous bug fixes
• Plot
  • Added box plot
  • Added histogram

New Releases

2022 Release

Overview

This release is the third major refactoring of Lisp-Stat, with particular emphasis on plotting. I have attempted to make plotting as easy in Lisp-Stat as it is in Vega-Lite, upon which it is based. In addition, much clean-up and polishing of sharp edges has taken place as a result of using Lisp-Stat on real-world projects.

As always, comments and contributions are welcome.

What’s New

Lisp-Stat

• Data loading functions akin to R’s data function. You can now load the Lisp-Stat data sets with this function, e.g. (data :mtcars). The mechanism is extensible for add on packages to use as well.
• Additional cleansed and annotated data sets included.

Plot

• The plotting system has been completely reworked. For example, to create a scatter plot of horsepower vs. miles per gallon:

(asdf:load-system :plot/vega)
(data :vgcars)
(plot:plot
  (vega:defplot hp-mpg
  `(:title "Horsepower vs. MPG"
    :data (:values ,vgcars)
    :mark :point
	:encoding (:x (:field vgcars:horsepower)
	           :y (:field vgcars:miles-per-gallon)))))

• 50+ new plotting examples for commonly used statistical plots, along with a plotting tutorial.

Data-frame

• Paid technical debt, refactored, made ready for future progress.
• Added filter-rows, remove-columns, remove-columns! and rename-column!.
• Improved unit tests.
• Added column types: categorical (factor) and temporal.
• Improved error handling, conditions and restarts.
• Working prototypes for stack, split-apply-combine, sort operations.

Array Operations

Array operations has been rehomed and now lives in the Lisp-Stat github organisation. Significant improvements to documentation over the previous repository.

Documentation

There probably isn’t a part of the documentation that hasn’t been touched. Improvements in all areas, including organisation, expanded topic coverage and quality improvements.

Semantic Versioning

Previously Lisp-Stat used semantic versioning appropiate for binary patch distribution. In this scheme the major number indicates whether or not the lisp image needed to be cold started, and a minor version indicating if a patch could be applied. Since it’s unlikely we’ll have a viable patching system in the open source Common Lisp world anytime soon, we’ve moved to semantic versioning for source releases. Since the semantics of versioning are different, all systems have been reset to 1.0.0. Going forward we’ll use the major.minor.patch scheme.