Penalized Classification using Fisher's Linear Discriminant
Implements the penalized LDA proposal of "Witten and Tibshirani (2011), Penalized classification using Fisher's linear discriminant, to appear in Journal of the Royal Statistical Society, Series B".

Feature Selection SVM using penalty functions
Natalia Becker, Wiebke Werft, Axel Benner
This package provides feature selection SVM using penalty functions. The smoothly clipped absolute deviation (SCAD) and 'L1-norm' penalties are availible up to now. Other functions will be implemented in the near feature.

Feature Selection SVM using Penalty Functions
e1071, MASS, corpcor, statmod, tgp, mlegp, lhs
Provides feature selection SVM using penalty
functions. The smoothly clipped absolute deviation (SCAD),
'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
SCAD' (SCAD and 'L2-norm') penalties are available. The tuning
parameters can be found using either a fixed grid or a interval
search.

Support Vector Machine (SVM) classification with simultaneous feature selection using penalty
functions is implemented. The smoothly clipped absolute deviation (SCAD),
'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
SCAD' (SCAD and 'L2-norm') penalties are available. The tuning
parameters can be found using either a fixed grid or a interval
search.

This package provides feature selection SVM using penalty
functions. The smoothly clipped absolute deviation (SCAD),
'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
SCAD' (SCAD and 'L2-norm') penalties are availible. The tuning
parameters can be founf using either a fixed grid or a interval
search.

Integrative Penalized Conditional Logistic Regression
c(
person(given = "Vera",
family = "Djordjilovi\'c",
role = c("aut", "cre"),
email = "vera.djordjilovic@unive.it",
comment = c(ORCID = "https://orcid.org/0000-0002-7670-3111")),
person(given = "Erica",
family = "Ponzi",
role = c("aut"),
email = "erica.ponzi@medisin.uio.no"))
Implements L1 and L2 penalized conditional logistic regression with penalty
factors allowing for integration of multiple data sources. Implements
stability selection for variable selection.
penalized, survival, clogitL1, stats, tidyverse
Vera Djordjilovi'c [aut, cre] (<https://orcid.org/0000-0002-7670-3111>),
Erica Ponzi [aut]