Packaged: Tue Oct  7 10:46:19 2008; DBarkauskas

Packaged: Fri Feb 13 14:41:23 2009; DBarkauskas

Title: Programs for Analyzing Fourier Transform-Ion Cyclotron Resonance
Description: This package was developed partially with funding from the
NIH Training Program in Biomolecular Technology

Title: FTRL Proximal Implementation for Elastic Net Regression
Authors@R: person("Vilhelm", "von Ehrenheim", email = "vonehrenheim@gmail.com", role = c("aut", "cre"))
Description: 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.
Author: Vilhelm von Ehrenheim [aut, cre]
Maintainer: Vilhelm von Ehrenheim <vonehrenheim@gmail.com>

Description: 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.

Title: Genomic Latent Dirichlet Allocation
Description: A tool for fitting latent Dirichlet allocation models to genomic annotation data.
Author: Daniel Backenroth and Iuliana Ionita-Laza

Title: Functional Tangential Angle Pseudo-Depth
Description: 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.

Title: Selecting variables in regression models
Author: Marta Sestelo, Nora M. Villanueva, Javier Roca-Pardinas
Description: FWDselect package introduces a simple method to select the
best model or best subset of variables using different types of
data (binary, gaussian or poisson) and applying it in different

data (continuous, binary or poisson) and applying it in
different contexts (parametric or nonparametric).

Title: Selecting Variables in Regression Models
person("Marta", "Sestelo",email= "sestelo@uvigo.es", role = c("aut", "cre")),
person("Nora", "M. Villanueva",email= "nmvillanueva@uvigo.es", role = "aut"),
person("Javier", "Roca-Pardinas",email= "roca@uvigo.es", role = "aut"))
to select the best model or best subset of variables using
different types of data (binary, Gaussian or Poisson) and
applying it in different contexts (parametric or non-parametric).
Imports: cvTools, mgcv, parallel, graphics, stats