Feature subset selection algorithms modularized in search algorithms and measure utilities. Full list and more information available at <https://dicits.ugr.es/software/FSinR/>.
c(
person("Alfonso",     "Jiménez-Vílchez", email = "i52jivia@uco.es", role = c("aut", "cre")),
person("Francisco",   "Aragón-Royón", email = "far@decsai.ugr.es", role = "aut"),
person("Adan M.",     "Rodriguez", email = "i32muroa@gmail.com", role = "aut"),
person("Antonio",     "Arauzo-Azofra", email = "arauzo@uco.es", role = "aut"),
person("José Manuel", "Benítez", email = "J.M.Benitez@decsai.ugr.es", role = "aut"))
rpart, neuralnet, class, digest, caret, mlbench, Rdpack, GA,
dplyr, tidyr, prodlim, rlang, purrr, e1071
Alfonso Jiménez-Vílchez [aut, cre],
Francisco Aragón-Royón [aut],
Adan M. Rodriguez [aut],
Antonio Arauzo-Azofra [aut],
José Manuel Benítez [aut]

Programs for Analyzing Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Data
This package was developed partially with funding from the NIH Training Program in Biomolecular Technology (2-T32-GM08799).

Programs for Analyzing Fourier Transform-Ion Cyclotron Resonance
Mass Spectrometry Data
This package was developed partially with funding from the
NIH Training Program in Biomolecular Technology
(2-T32-GM08799).

FTRL Proximal Implementation for Elastic Net Regression
person("Vilhelm", "von Ehrenheim", email = "vonehrenheim@gmail.com", role = c("aut", "cre"))
Implementation of Follow The Regularized Leader (FTRL) Proximal algorithm used for online training of large scale regression models using a mixture of L1 and L2 regularization.

Implementation of Follow The Regularized Leader (FTRL) Proximal algorithm, proposed by McMahan et al. (2013) <doi:10.1145/2487575.2488200>, used for online training of large scale regression models using a mixture of L1 and L2 regularization.

A tool for fitting latent Dirichlet allocation models to genomic annotation data.
Daniel Backenroth and Iuliana Ionita-Laza

Computes the functional tangential angle pseudo-depth and its robustified version from the paper by Kuhnt and Rehage (2016). See Kuhnt, S.; Rehage, A. (2016): An angle-based multivariate functional pseudo-depth for shape outlier detection, JMVA 146, 325-340, <doi:10.1016/j.jmva.2015.10.016> for details.