spFSR: Feature Selection and Ranking via Simultaneous Perturbation Stochastic Approximation

An implementation of feature selection, weighting and ranking via simultaneous perturbation stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best predictive performance using some error measures such as mean squared error (for regression problems) and accuracy rate (for classification problems).

Version: 2.0.4
Depends: mlr3 (≥ 0.14.0), future (≥ 1.28.0), tictoc (≥ 1.0)
Imports: mlr3pipelines (≥ 0.4.2), mlr3learners (≥ 0.5.4), ranger (≥ 0.14.1), parallel (≥ 3.4.2), ggplot2 (≥ 2.2.1), lgr (≥ 0.4.4)
Suggests: caret (≥ 6.0), MASS (≥ 7.3)
Published: 2023-03-17
DOI: 10.32614/CRAN.package.spFSR
Author: David Akman [aut, cre], Babak Abbasi [aut, ctb], Yong Kai Wong [aut, ctb], Guo Feng Anders Yeo [aut, ctb], Zeren D. Yenice [ctb]
Maintainer: David Akman <david.v.akman at gmail.com>
BugReports: https://github.com/yongkai17/spFSR/issues
License: GPL-3
URL: https://www.featureranking.com/
NeedsCompilation: no
CRAN checks: spFSR results


Reference manual: spFSR.pdf


Package source: spFSR_2.0.4.tar.gz
Windows binaries: r-devel: spFSR_2.0.4.zip, r-release: spFSR_2.0.4.zip, r-oldrel: spFSR_2.0.4.zip
macOS binaries: r-release (arm64): spFSR_2.0.4.tgz, r-oldrel (arm64): spFSR_2.0.4.tgz, r-release (x86_64): spFSR_2.0.4.tgz, r-oldrel (x86_64): spFSR_2.0.4.tgz
Old sources: spFSR archive


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