Robert B. Gramacy <rbg@vt.edu>, Furong Sun <furongs@vt.edu>

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 provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided.

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 provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided.  For details and tutorial, see Gramacy (2016 <doi:10.18637/jss.v072.i01>.

mvtnorm, MASS, interp, lhs, crs, DiceOptim

Laboratory for Dynamic Synthetic Vegephenomenology
A variety of ordination and vegetation analyses
useful in analysis of datasets in community ecology.  Includes
many of the common ordination methods, with graphical
routines to facilitate their interpretation, as well as
several novel analyses.

David W. Roberts <droberts@montana.edu>, with extraordinary
help from Jari Oksanen

Ordination and Multivariate Analysis for Ecology

A variety of ordination and vegetation analyses useful in
analysis of datasets in community ecology.  Includes many of
the common ordination methods, with graphical routines to
facilitate their interpretation, as well as several novel
analyses.