Data smoothers for Common Lisp
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Nonparametric regression offers a flexible alternative to classic (parametric) methods for regression. Unlike parametric methods, which assume that the regression relationship has a known form that depends on a finite number of unknown parameters, nonparametric regression models attempt to learn the form of the regression relationship from a sample of data.
All nonparametric regression models involve finding some balance between fitting the observed sample of data (model fit) and “smoothing” the function estimate (model parsimony). Typically, this balance is determined using some form of cross-validation, which attempts to find a function estimate that does well for predicting new data. As a result, nonparametric regression models can be useful for discovering relationships between variables, as well as for developing generalizable prediction rules.
To get a local copy up and running follow these steps:
An ANSI Common Lisp implementation. Developed and tested with SBCL.
To make the system accessible to ASDF (a build facility, similar to make in the C world), clone the repository in a directory ASDF knows about. By default the common-lisp directory in your home directory is known. Create this if it doesn't already exist and then:
- Clone the repositories
cd ~/common-lisp && \
git clone https://github.com/Lisp-Stat/smoothers- Reset the ASDF source-registry to find the new system (from the REPL)
(asdf:clear-source-registry) - Load the system
(asdf:load-system :smoothers)
If you have installed the slime ASDF extensions, you can invoke this with a comma (',') from the slime REPL.
To get the third party systems that smoothers depends on, you can use a dependency manager, such as Quicklisp or CLPM. Once installed, get the dependencies with either of:
(clpm-client:sync :sources "smoothers") ;sources may vary(ql:quickload :smoothers)You need do this only once. After obtaining the dependencies, you can
load the system with ASDF as described above without first syncing
sources.
Nonparametric Regression (Smoothers) in R
See the open issues for a list of proposed features (and known issues).
This system is part of the Lisp-Stat project; that should be your first stop for information. Also see the resources and community page for more information.
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated. Please see CONTRIBUTING for details on the code of conduct, and the process for submitting pull requests. Good first issues include porting the xlisp-stat super-smoother to Common Lisp and bringing in Gary King's smoothing.lisp.
Distributed under the MS-PL License. See LICENSE for more information.
Project Link: https://github.com/lisp-stat/smootherfs