Package: spFSR Type: Package Title: Feature Selection and Ranking by Simultaneous Perturbation Stochastic Approximation Version: 1.0.0 Date: 2018-05-10 Authors@R: c( person("Vural", "Aksakalli", email = "vaksakalli@gmail.com", role = c("aut", "cre")), person("Babak", "Abbasi", email = "babak.abbasi@rmit.edu.au", role = c("aut", "ctb")), person("Yong Kai", "Wong", email = "yongkai1017@gmail.com", role = c("aut", "ctb")), person("Zeren", "D. Yenice", email = "zerendyenice@gmail.com", role = "ctb")) Description: An implementation of feature selection and ranking via simultaneous perturbation stochastic approximation (SPSA-FSR) based on works by V. Aksakalli and M. Malekipirbazari (2015) and Zeren D. Yenice and et al. (2018) . The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best predictive performance using a specified error measure such as mean squared error (for regression problems) and accuracy rate (for classification problems). This package requires an object of class 'task' and an object of class 'Learner' from the 'mlr' package. License: GPL-3 Encoding: UTF-8 Depends: mlr (>= 2.11), parallelMap (>= 1.3), parallel (>= 3.4.2), tictoc (>= 1.0) Imports: ggplot2 (>= 2.2.1), class (>= 7.3), mlbench (>= 2.1) Suggests: caret (>= 6.0), MASS (>= 7.3), knitr, rmarkdown URL: https://www.featureranking.com/, https://arxiv.org/abs/1804.05589 BugReports: https://github.com/yongkai17/spFSR/issues RoxygenNote: 6.0.1 VignetteBuilder: knitr Config/pak/sysreqs: libgdal-dev gdal-bin libgeos-dev libglu1-mesa-dev libgmp3-dev libgsl0-dev jags libicu-dev libxml2-dev libmpfr-dev libopenmpi-dev libproj-dev Repository: https://yongkai17.r-universe.dev Date/Publication: 2018-10-02 12:14:06 UTC RemoteUrl: https://github.com/yongkai17/spfsr RemoteRef: HEAD RemoteSha: 0ca92c1d7175b2b7a735647828b00bc2e5e9d95f NeedsCompilation: no Packaged: 2026-09-20 07:33:10 UTC; root Author: Vural Aksakalli [aut, cre], Babak Abbasi [aut, ctb], Yong Kai Wong [aut, ctb], Zeren D. Yenice [ctb] Maintainer: Vural Aksakalli