Description: Functions to compute split generalized linear models. The approach fits
generalized linear models that split the covariates into groups. The
optimal split of the variables into groups and the regularized estimation
of the coefficients are performed by minimizing an objective function
that encourages sparsity within each group and diversity among them.

Suggests: testthat, vctrs, mvnfast, glmnet

Example applications can be found in Christidis et al. (2021)

Title: Split Knockoffs for Structural Sparsity
Yang Cao [aut] (Revison of this package),
Xinwei Sun [aut] (Original ideas about the package),
Yuan Yao [aut] (Testing for the package and management of the
person("Xinwei", "Sun", role = c("aut"),
comment="Original ideas about the package"),
comment="Testing for the package and management of the development"))
Description: A novel method for controlling the false discovery rate (FDR)
in structural sparsity setting. This proposed scheme relaxes the linear subspace
constraint to its neighborhood, often known as variable splitting in optimization.
Simulation experiments can be reproduced following the Vignette.
'Split Knockoffs' is defined in Cao et al. (2021) <arXiv:2103.16159>.
Imports: glmnet, MASS, latex2exp, RSpectra, ggplot2, Matrix, stats,

Simulation experiments can be reproduced following the Vignette. We include data
(both .mat and .csv format) and application with our method of Alzheimer’s Disease

Description: Split Knockoff is a data adaptive variable selection framework for controlling the
(directional) false discovery rate (FDR) in structural sparsity, where variable
selection on linear transformation of parameters is of concern. This proposed scheme
relaxes the linear subspace constraint to its neighborhood, often known as variable
(both .mat and .csv format) and application with our method of Alzheimer's Disease
'Split Knockoffs' is first defined in Cao et al. (2021) <arXiv:2103.16159>.

Description: Functions for computing split regularized estimators defined in Christidis, Lakshmanan,
Smucler and Zamar (2019) <arXiv:1712.03561>. The approach fits linear regression models that
split the set of covariates into groups. The optimal split of the variables into groups and the
regularized estimation of the regression coefficients are performed by minimizing  an objective
function that encourages sparsity within each group and diversity among them.
The estimated coefficients are then pooled together to form the final fit.