Rcpp (>= 0.12.5), stats, dplyr, graphics, igraph, Matrix,
aricode, grDevices, glmnet, ggplot2, cvTools

Rcpp (>= 0.12.5), stats, dplyr, graphics, igraph, Matrix,
aricode, grDevices, caret, glmnet, ggplot2, cvTools

Provides an l1-version of the spectral clustering algorithm devoted to robustly clustering highly perturbed graphs using l1-penalty. This algorithm is described with more details in the preprint C. Champion, M. Champion, M. Blazère, R. Burcelin and J.M. Loubes, "l1-spectral clustering algorithm: a spectral clustering method using l1-regularization" (2022).

Friedman's Boosting Algorithm for Regularized Linear Regression
c(person("John", "Ehrlinger", role=c("aut", "cre"),
email="john.ehrlinger@gmail.com"), person("Hemant", "Ishwaran",
role="aut"))
Efficient implementation of Friedman's boosting algorithm with
l2-loss function and coordinate direction (design matrix columns) basis
functions.
John Ehrlinger [aut, cre],
Hemant Ishwaran [aut]

Exploring Friedman's Boosting Algorithm for Regularized Linear
Regression

l2boost - Friedman's boosting algorithm for regularized linear
regression
Efficient implementation of Friedman's boosting algorithm
with l2-loss function and coordinate direction (design matrix
columns) basis functions.
John Ehrlinger [aut, cre], Hemant Ishwaran [aut]
'VAR.R' 'break.ties.R' 'coef.l2boost.R' 'cv.l2boost.R'
'diabetes.R' 'elasticNetSim.R' 'error.bars.R' 'eval.fold.R'
'extract.corr.R' 'fitted.l2boost.R' 'get.discrete.solution.R'
'get.hybrid.solution.R' 'get.lars.solution.R' 'help.R'
'l2boost.R' 'mstep.long.R' 'mvnorm.l2boost.R' 'nu.limit.R'
'plot.l2boost.R' 'plot.lines.R' 'predict.l2boost.R'
'print.l2boost.R' 'print.summary.l2boost.R' 'resample.R'
'residuals.l2boost.R' 'summary.l2boost.R' 'which.max.ind.R'
'which.min.ind.R'

Local Approximate Gaussian Process Regression
Performs approximate GP regression for large computer experiments and spatial datasets.  The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine.  OpenMP and GPU features may require special compilation.  An interface to lower-level (full) GP inference and prediction is also provided.