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 | Deprecated fit function |
msgl.algorithm.config | Create a new algorithm configuration |
msgl.c.config | Featch information about the C side configuration of the... |
msgl.cv | Deprecated cv function |
msgl_dense_sgl_fit_R | C interface |
msgl_dense_sgl_lambda_R | C interface |
msgl_dense_sgl_predict_R | C interface |
msgl_dense_sgl_subsampling_R | C interface |
msgl.lambda.seq | Deprecated lambda function |
msgl-package | Multinomial logistic regression with sparse group lasso... |
msgl_sparse_sgl_fit_R | C interface |
msgl_sparse_sgl_lambda_R | C interface |
msgl_sparse_sgl_predict_R | C interface |
msgl_sparse_sgl_subsampling_R | C interface |
msgl.standard.config | Standard msgl algorithm configuration |
msgl.subsampling | Deprecated subsampling function |
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 |
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