gglasso - Group Lasso Penalized Learning Using a Unified BMD Algorithm
A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: <doi:10.1007/s11222-014-9498-5>.
Last updated 5 years ago
fortran
8.07 score 10 stars 9 dependents 292 scripts 571 downloadsgcdnet - The (Adaptive) LASSO and Elastic Net Penalized Least Squares, Logistic Regression, Hybrid Huberized Support Vector Machines, Squared Hinge Loss Support Vector Machines and Expectile Regression using a Fast Generalized Coordinate Descent Algorithm
Implements a generalized coordinate descent (GCD) algorithm for computing the solution paths of the hybrid Huberized support vector machine (HHSVM) and its generalizations. Supported models include the (adaptive) LASSO and elastic net penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.
Last updated 2 years ago
fortran
4.90 score 7 stars 2 dependents 63 scripts 289 downloadsglmvsd - Variable Selection Deviation Measures and Instability Tests for High-Dimensional Generalized Linear Models
Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.
Last updated 2 years ago
2.78 score 3 stars 5 scripts 279 downloadsfastcox - Lasso and Elastic-Net Penalized Cox's Regression in High Dimensions Models using the Cocktail Algorithm
We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) DOI: <doi:10.4310/SII.2013.v6.n2.a1>.
Last updated 7 years ago
fortran
2.71 score 17 scripts 476 downloadsSOIL - Sparsity Oriented Importance Learning
Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+).
Last updated 7 years ago
2.30 score 1 stars 20 scripts 478 downloads