spca
(Package: elasticnet) :
Sparse Principal Components Analysis
Using an alternating minimization algorithm to minimize the SPCA criterion.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: spca
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print.spca
(Package: elasticnet) :
Print method for spca objects
Print out a spca fit.
● Data Source:
CranContrib
● Keywords: methods
● Alias: print.spca
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print.enet
(Package: elasticnet) :
Print method for enet objects
Print out an enet fit.
● Data Source:
CranContrib
● Keywords: methods
● Alias: print.enet
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Print out an arrayspc fit.
● Data Source:
CranContrib
● Keywords: methods
● Alias: print.arrayspc
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While enet() produces the entire path of solutions, predict.enet allows one to extract a prediction at a particular point along the path.
● Data Source:
CranContrib
● Keywords: methods, regression
● Alias: predict.enet
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plot.enet
(Package: elasticnet) :
Plot method for enet objects
Produce a plot of an enet fit. The default is a complete coefficient path.
● Data Source:
CranContrib
● Keywords: hplot, methods
● Alias: plot.enet
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Starting from zero, the LARS-EN algorithm provides the entire sequence of coefficients and fits.
● Data Source:
CranContrib
● Keywords: regression
● Alias: enet
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Internal elasticnet functions
● Data Source:
CranContrib
● Keywords: internal
● Alias: rootmatrix, soft, solvebeta, updateRR
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cv.enet
(Package: elasticnet) :
Computes K-fold cross-validated error curve for elastic net
Computes the K-fold cross-validated mean squared prediction error for elastic net.
● Data Source:
CranContrib
● Keywords: regression
● Alias: cv.enet
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arrayspc
(Package: elasticnet) :
Sparse PCs of Microarrays
Sparse PC by iterative SVD and soft-thresholding
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: arrayspc
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providing 
.