
SINDy
 Referenced in 18 articles
[sw30277]
 Constrained sparse Galerkin regression. The sparse identification of nonlinear dynamics (SINDy) is a recently proposed ... datadriven modelling framework that uses sparse regression techniques to identify nonlinear loworder models ... measurement data and satisfies necessary constraints. Galerkin regression models also readily generalize to include higher ... entire code base for our constrained sparse Galerkin regression algorithm is freely available online...

SparseLOGREG
 Referenced in 26 articles
[sw11449]
 Efficient Algorithm for Gene Selection using Sparse Logistic Regression. Motivation: This paper gives ... efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based...

ARock
 Referenced in 27 articles
[sw16800]
 decentralized consensus problems. Numerical experiments solving sparse logistic regression problems are presented...

LIBLINEAR
 Referenced in 135 articles
[sw04880]
 largescale linear classification. It supports logistic regression and linear support vector machines. We provide ... that LIBLINEAR is very efficient on large sparse data sets...

foba
 Referenced in 27 articles
[sw35840]
 forward, backward, and foba sparse learning algorithms for ridge regression, described in the paper ”Adaptive...

TIGRESS
 Referenced in 9 articles
[sw23826]
 formulate GRN inference as a sparse regression problem and investigate the performance of a popular...

msgl
 Referenced in 10 articles
[sw26552]
 Multinomial Sparse Group Lasso. Multinomial logistic regression with sparse group lasso penalty. Simultaneous feature selection...

spls
 Referenced in 10 articles
[sw09507]
 package spls: Sparse Partial Least Squares (SPLS) Regression and Classification. This package ... provides functions for fitting a Sparse Partial Least Squares Regression and Classification...

SVMlight
 Referenced in 261 articles
[sw04076]
 pattern recognition, for the problem of regression, and for the problem of learning a ranking ... applications. Many tasks have the property of sparse instance vectors. This implementation makes...

OOQP
 Referenced in 39 articles
[sw04743]
 including general sparse QPs, QPs arising from support vector machines, Huber regression problems...

robustHD
 Referenced in 5 articles
[sw14244]
 based on least angle regression and sparse regression...

camel
 Referenced in 9 articles
[sw14318]
 linear models; (2) Calibrated Multivariate Regression for estimating sparse multivariate linear models; (3) Tiger, Calibrated...

SpaSM
 Referenced in 4 articles
[sw23903]
 Matlab toolbox for performing sparse regression, classification and principal component analysis. The toolbox has been...

cobs
 Referenced in 9 articles
[sw21863]
 cobs: Constrained BSplines (Sparse Matrix Based). Qualitatively Constrained (Regression) Smoothing Splines via Linear Programming...

flare
 Referenced in 18 articles
[sw12406]
 flare: Family of Lasso Regression. The package ”flare” provides the implementation of a family ... Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction...

SOFAR
 Referenced in 4 articles
[sw31665]
 suggest the method of sparse orthogonal factor regression (SOFAR) via the sparse singular value decomposition...

mixOmics
 Referenced in 29 articles
[sw09508]
 highly dimensional data sets: regularized CCA and sparse PLS to unravel relationships between two heterogeneous ... step procedure and two frameworks are proposed: regression and canonical analysis. Numerous graphical outputs ... interpreting the results. Recent methodological developments include: sparse PLSDiscriminant Analysis, Independent Principal Component Analysis...

PySINDy
 Referenced in 2 articles
[sw33019]
 systems from data. PySINDy is a sparse regression package with several implementations for the Sparse...

StOpt
 Referenced in 7 articles
[sw32903]
 based on Monte Carlo with regressions (global, local and sparse regressors), for underlying states following...

StingyCD
 Referenced in 2 articles
[sw37122]
 computationally wasteful. Due to sparsity in sparse regression problems, for example, often the majority...