Elasticnet

method = 'enet'

Type: Regression

Tuning Parameters: fraction (Fraction of Full Solution), lambda (Weight Decay)

glmnet

method = 'glmnet'

Type: Regression, Classification

Tuning Parameters: alpha (Mixing Percentage), lambda (Regularization Parameter)

Least Angle Regression

method = 'lars'

Type: Regression

Tuning Parameters: fraction (Fraction)

Least Angle Regression

method = 'lars2'

Type: Regression

Tuning Parameters: step (#Steps)

Penalized Linear Discriminant Analysis

method = 'PenalizedLDA'

Type: Classification

Tuning Parameters: lambda (L1 Penalty), K (#Discriminant Functions)

Penalized Linear Regression

method = 'penalized'

Type: Regression

Tuning Parameters: lambda1 (L1 Penalty), lambda2 (L2 Penalty)

Relaxed Lasso

method = 'relaxo'

Type: Regression

Tuning Parameters: lambda (Penalty Parameter), phi (Relaxation Parameter)

Sparse Linear Discriminant Analysis

method = 'sparseLDA'

Type: Classification

Tuning Parameters: NumVars (# Predictors), lambda (Lambda)

Sparse Mixture Discriminant Analysis

method = 'smda'

Type: Classification

Tuning Parameters: NumVars (# Predictors), lambda (Lambda), R (# Subclasses)

Sparse Partial Least Squares

method = 'spls'

Type: Regression, Classification

Tuning Parameters: K (#Components), eta (Threshold), kappa (Kappa)

The lasso

method = 'lasso'

Type: Regression

Tuning Parameters: fraction (Fraction of Full Solution)

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