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sglOptim (version 1.3.8)

Generic Sparse Group Lasso Solver

Description

Fast generic solver for sparse group lasso optimization problems. The loss (objective) function must be defined in a C++ module. The optimization problem is solved using a coordinate gradient descent algorithm. Convergence of the algorithm is established (see reference) and the algorithm is applicable to a broad class of loss functions. Use of parallel computing for cross validation and subsampling is supported through the 'foreach' and 'doParallel' packages. Development version is on GitHub, please report package issues on GitHub.

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Version

Install

install.packages('sglOptim')

Monthly Downloads

75

Version

1.3.8

License

GPL (>= 2)

Issues

0

Pull Requests

0

Stars

7

Forks

1

Maintainer

Niels Richard Hansen

Last Published

May 7th, 2019

Functions in sglOptim (1.3.8)

create.sgldata

Create a sgldata object
linear_test_block_diagonal_sgl_fit_R

C interface
Err

Generic function for computing error rates
best_model

Index of best model
best_model.sgl

Index of best model
linear_test_block_diagonal_spx_sgl_fit_R

C interface
Err.sgl

Error Rates
linear_test_block_diagonal_sgl_predict_R

C interface
features

Extracts nonzero features
linear_test_block_diagonal_spx_sgl_lambda_R

C interface
linear_test_block_diagonal_sgl_subsampling_R

C interface
linear_test_diagonal_w_spx_spy_sgl_fit_R

C interface
linear_test_diagonal_w_spx_spy_sgl_lambda_R

C interface
add_data

Add data to a sgldata data object
linear_test_block_diagonal_sgl_test_R

C interface
linear_test_diagonal_w_spy_sgl_lambda_R

C interface
features.sgl

Extracting nonzero features
linear_test_block_diagonal_spx_sgl_predict_R

C interface
linear_test_block_diagonal_spx_spy_sgl_test_R

C interface
linear_test_block_diagonal_spx_spy_sgl_subsampling_R

C interface
linear_test_diagonal_error_w_sgl_lambda_R

C interface
linear_test_block_diagonal_spx_sgl_subsampling_R

C interface
linear_test_block_diagonal_spy_sgl_test_R

C interface
linear_test_diagonal_error_w_sgl_fit_R

C interface
linear_test_diagonal_error_w_sgl_test_R

C interface
linear_test_diagonal_w_spy_sgl_predict_R

C interface
linear_test_full_spx_spy_sgl_fit_R

C interface
linear_test_diagonal_w_spy_sgl_subsampling_R

C interface
linear_test_diagonal_w_spy_sgl_test_R

C interface
linear_test_identity_sgl_test_R

C interface
linear_test_diagonal_w_spx_sgl_lambda_R

C interface
linear_test_diagonal_w_spx_sgl_predict_R

C interface
linear_test_block_diagonal_spx_spy_sgl_lambda_R

C interface
linear_test_full_sgl_test_R

C interface
linear_test_full_spx_sgl_fit_R

C interface
features_stat

Extract feature statistics
add_data.sgldata

Add data to a sgldata data object
features_stat.sgl

Extract feature statistics
linear_test_block_diagonal_spx_spy_sgl_predict_R

C interface
linear_test_block_diagonal_spx_sgl_test_R

C interface
linear_test_block_diagonal_spx_spy_sgl_fit_R

C interface
linear_test_diagonal_w_sgl_fit_R

C interface
linear_test_diagonal_w_sgl_lambda_R

C interface
linear_test_diagonal_w_spx_sgl_subsampling_R

C interface
linear_test_diagonal_w_spx_sgl_test_R

C interface
linear_test_full_sgl_fit_R

C interface
linear_test_full_sgl_lambda_R

C interface
linear_test_block_diagonal_spy_sgl_subsampling_R

C interface
linear_test_block_diagonal_spy_sgl_predict_R

C interface
linear_test_full_spy_sgl_subsampling_R

C interface
linear_test_diagonal_w_sgl_predict_R

C interface
linear_test_diagonal_w_sgl_subsampling_R

C interface
linear_test_full_spy_sgl_test_R

C interface
linear_test_full_spx_sgl_predict_R

C interface
linear_test_full_spx_sgl_lambda_R

C interface
linear_test_full_spy_sgl_lambda_R

C interface
linear_test_full_spy_sgl_predict_R

C interface
linear_test_identity_spx_sgl_subsampling_R

C interface
linear_test_identity_spx_sgl_fit_R

C interface
linear_test_identity_spx_sgl_test_R

C interface
linear_test_identity_spy_sgl_lambda_R

C interface
linear_test_identity_spx_spy_sgl_fit_R

C interface
linear_test_diagonal_w_spx_spy_sgl_subsampling_R

C interface
linear_test_diagonal_w_spx_spy_sgl_predict_R

C interface
linear_test_identity_spy_sgl_predict_R

C interface
linear_test_full_spx_spy_sgl_lambda_R

C interface
linear_test_identity_spx_spy_sgl_lambda_R

C interface
coef.sgl

Extracting the nonzero coefficients
prepare.args

Generic function for preparing the sgl call arguments
prepare.args.sgldata

Prepare sgl function arguments
linear_test_full_sgl_predict_R

C interface
linear_test_identity_spx_spy_sgl_predict_R

C interface
linear_test_full_sgl_subsampling_R

C interface
sgl.algorithm.config

Create a new algorithm configuration
sgl.c.config

Featch information about the C side configuration of the package
linear_test_identity_sgl_fit_R

C interface
models

Extract fitted models
models.sgl

Extract the estimated models
sgl_test

Test a sgl-Objective
sparseMatrix_from_C_format

Convert to sparse matrix
compute_error

Helper function for computing error rates
parameters_stat

Extract parameter statistics
linear_test_identity_spx_spy_sgl_subsampling_R

C interface
linear_test_identity_sgl_lambda_R

C interface
prepare_data

Prepare a sgldata data object
parameters_stat.sgl

Extracting parameter statistics
linear_test_block_diagonal_spy_sgl_lambda_R

C interface
linear_test_block_diagonal_spy_sgl_fit_R

C interface
linear_test_full_spx_spy_sgl_test_R

C interface
linear_test_full_spy_sgl_fit_R

C interface
sgl_cv

Generic sparse group lasso cross validation using multiple possessors
linear_test_diagonal_w_sgl_test_R

C interface
linear_test_diagonal_w_spx_spy_sgl_test_R

C interface
linear_test_diagonal_w_spx_sgl_fit_R

C interface
linear_test_diagonal_w_spy_sgl_fit_R

C interface
linear_test_identity_sgl_predict_R

C interface
linear_test_identity_sgl_subsampling_R

C interface
sgl_fit

Fit a Sparse Group Lasso Regularization Path.
linear_test_identity_spy_sgl_subsampling_R

C interface
sgl_print

Print information about sgl object
linear_test_full_spx_sgl_subsampling_R

C interface
linear_test_identity_spy_sgl_test_R

C interface
linear_test_full_spx_sgl_test_R

C interface
sgl_subsampling

Generic sparse group lasso subsampling procedure
print_with_metric_prefix

Print a numeric with metric prefix
linear_test_full_spx_spy_sgl_predict_R

C interface
linear_test_full_spx_spy_sgl_subsampling_R

C interface
nmod

Number of models used for fitting
sgl.standard.config

Standard algorithm configuration
linear_test_identity_spx_sgl_lambda_R

C interface
linear_test_identity_spx_sgl_predict_R

C interface
nmod.sgl

Returns the number of models in a sgl object
sgl_lambda_sequence

Computing a Lambda Sequence
linear_test_identity_spx_spy_sgl_test_R

C interface
linear_test_identity_spy_sgl_fit_R

C interface
sgl_predict

Predict
sglOptim

sglOptim: Generic Sparse Group Lasso Solver
parameters

Extracts nonzero parameters
parameters.sgl

Extracting nonzero parameters
sparseMatrix_to_C_format

Prepare sparse matrix for .Call
test_rtools

Test internal rtools
subsample

Subsample
transpose_response_elements

Transpose response elements
rearrange

Generic rearrange function
rearrange.sgldata

Rearrange sgldata
subsample.sgldata

Subsample sgldata
test.data

Simulated data set
get_coef

Get the nonzero coefficients
element_class

Retur the element class of an object.
linear_test_block_diagonal_sgl_lambda_R

C interface