Uses gradient descent to minimize the sum of squared
residuals for the regression problem.  Can include an L2
penalty on the coefficient matrix. This function is very useful
when there is an initial guess of what B should be in Y = XB.
In general, this function performs faster than R's lm function.
GPU acceleration can be used to make this function extremely
fast.  This package suggests cudaMatrixOps, which is not on
CRAN but can be downloaded at stanford.edu/~jeffwong.

Modelling Interactions in High-Dimensional Data with
Backtracking
person("Rajen", "Shah", email = "r.shah@statslab.cam.ac.uk",
role = c("aut", "cre"))
Implementation of the algorithm introduced in "Shah, R. D.
(2016) Modelling interactions in high-dimensional data with Backtracking, JMLR,
to appear". Data with thousands of predictors can be handled. The algorithm
performs sequential Lasso (Tibshirani, 1996) fits on design matrices containing
increasing sets of candidate interactions. Previous fits are used to greatly
speed up subsequent fits so the algorithm is very efficient.

Implementation of the algorithm introduced in Shah, R. D.
(2016) <http://www.jmlr.org/papers/volume17/13-515/13-515.pdf>.
Data with thousands of predictors can be handled. The algorithm
performs sequential Lasso fits on design matrices containing
increasing sets of candidate interactions. Previous fits are used to greatly
speed up subsequent fits so the algorithm is very efficient.

Implementation of the algorithm introduced in Shah, R. D.
(2016) <https://www.jmlr.org/papers/volume17/13-515/13-515.pdf>.
Data with thousands of predictors can be handled. The algorithm
performs sequential Lasso fits on design matrices containing
increasing sets of candidate interactions. Previous fits are used to greatly
speed up subsequent fits, so the algorithm is very efficient.

High-Dimensional Lasso Generalized Estimating Equations
Fits generalized estimating equations with L1 regularization to longitudinal data with high dimensional covariates. Use a efficient iterative composite gradient descent algorithm.
Rcpp (>= 1.0.4), PGEE, MASS, mvtnorm, caret, SimCorMultRes