ARGOS: Automatic Regression for Governing Equations (ARGOS)

Comprehensive set of tools for performing system identification of both linear and nonlinear dynamical systems directly from data. The Automatic Regression for Governing Equations (ARGOS) simplifies the complex task of constructing mathematical models of dynamical systems from observed input and output data, supporting various types of systems, including those described by ordinary differential equations. It employs optimal numerical derivatives for enhanced accuracy and employs formal variable selection techniques to help identify the most relevant variables, thereby enabling the development of predictive models for system behavior analysis.

Version:	0.1.1
Depends:	R (≥ 3.6.0)
Imports:	Matrix, glmnet, Metrics, boot, tidyverse, magrittr, tidyr, signal, parallel, deSolve
Suggests:	testthat, knitr, rmarkdown, devtools
Published:	2024-01-18
DOI:	10.32614/CRAN.package.ARGOS
Author:	Kevin Egan [aut, cre], Weizhen Li [aut], Rui Carvalho [aut], Yuzheng Zhang [aut]
Maintainer:	Kevin Egan <kevin.egan at durham.ac.uk>
Contact:	Please report bugs and other issues to <kevin.egan@durham.ac.uk>.
License:	GPL-3
URL:	<https://github.com/kevinegan31/ARGOS-Package>
NeedsCompilation:	no
Materials:	README
CRAN checks:	ARGOS results

Documentation:

Reference manual:

ARGOS.pdf

Downloads:

Package source:	ARGOS_0.1.1.tar.gz
Windows binaries:	r-devel: ARGOS_0.1.1.zip, r-release: ARGOS_0.1.1.zip, r-oldrel: ARGOS_0.1.1.zip
macOS binaries:	r-release (arm64): ARGOS_0.1.1.tgz, r-oldrel (arm64): ARGOS_0.1.1.tgz, r-release (x86_64): ARGOS_0.1.1.tgz, r-oldrel (x86_64): ARGOS_0.1.1.tgz
Old sources:	ARGOS archive

Linking:

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