# High Dimensional Multiclass Classification Using Sparse Group Lasso

## Documentation for package ‘msgl’ version 2.3.6

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

msgl-package | Multinomial logistic regression with sparse group lasso penalty.
best_model.msgl | Index of best model
classes | Class vector
coef.msgl | Nonzero coefficients
cv | Cross Validation
Err.msgl | Compute error rates
features.msgl | Nonzero features
features_stat.msgl | Extract feature statistics
fit | Fit a multinomial sparse group lasso regularization path.
lambda | Computes a lambda sequence for the regularization path
models.msgl | Extract the fitted models
msgl.algorithm.config | Create a new algorithm configuration
msgl.c.config | Featch information about the C side configuration of the package
msgl.standard.config | Standard msgl algorithm configuration
nmod.msgl | Number of models used for fitting
parameters.msgl | Nonzero parameters
parameters_stat.msgl | Extracting parameter statistics
predict.msgl | Predict
PrimaryCancers | Primary cancer samples.
print.msgl | Print function for msgl
SimData | Simulated data set
subsampling | Multinomial sparse group lasso generic subsampling procedure
x | Design matrix