# Penalized Multivariate Analysis

## Documentation for package ‘PMA’ version 1.1

- DESCRIPTION file.

## Help Pages

PMA-package | Penalized Multivariate Analysis
breastdata | Breast cancer gene expression + DNA copy number data set from Chin et al (2006), Cancer Cell
CCA | Perform sparse canonical correlation analysis using the penalized matrix decomposition.
CCA.permute | Select tuning parameters for sparse canonical correlation analysis using the penalized matrix decomposition.
MultiCCA | Perform sparse multiple canonical correlation analysis.
MultiCCA.permute | Select tuning parameters for sparse multiple canonical correlation analysis using the penalized matrix decomposition.
plot.CCA.permute | Select tuning parameters for sparse canonical correlation analysis using the penalized matrix decomposition.
plot.MultiCCA.permute | Select tuning parameters for sparse multiple canonical correlation analysis using the penalized matrix decomposition.
plot.SPC.cv | Perform cross-validation on sparse principal component analysis
PlotCGH | Plot CGH data
PMA | Penalized Multivariate Analysis
PMD | Get a penalized matrix decomposition for a data matrix.
PMD.cv | Do tuning parameter selection for PMD via cross-validation
print.CCA | Perform sparse canonical correlation analysis using the penalized matrix decomposition.
print.CCA.permute | Select tuning parameters for sparse canonical correlation analysis using the penalized matrix decomposition.
print.MultiCCA | Perform sparse multiple canonical correlation analysis.
print.MultiCCA.permute | Select tuning parameters for sparse multiple canonical correlation analysis using the penalized matrix decomposition.
print.SPC | Perform sparse principal component analysis
print.SPC.cv | Perform cross-validation on sparse principal component analysis
SPC | Perform sparse principal component analysis
SPC.cv | Perform cross-validation on sparse principal component analysis