Title: Friedman's Boosting Algorithm for Regularized Linear Regression
Authors@R: c(person("John", "Ehrlinger", role=c("aut", "cre"),
Description: Efficient implementation of Friedman's boosting algorithm with
l2-loss function and coordinate direction (design matrix columns) basis

Title: Exploring Friedman's Boosting Algorithm for Regularized Linear

Title: l2boost - Friedman's boosting algorithm for regularized linear
Description: Efficient implementation of Friedman's boosting algorithm
with l2-loss function and coordinate direction (design matrix
Author: John Ehrlinger [aut, cre], Hemant Ishwaran [aut]
Collate: 'VAR.R' 'break.ties.R' 'coef.l2boost.R' 'cv.l2boost.R'

Title: Local Approximate Gaussian Process Regression
Author: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Description: 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.

Description: 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, as are associated wrapper routines for blackbox optimization under constraints via an augmented Lagrangian scheme

Suggests: mvtnorm, MASS, akima, lhs, crs
Description: 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, as are associated wrapper routines for blackbox optimization under constraints via an augmented Lagrangian scheme, and large scale computer model calibration