MASS, tools, utils, stats, methods, graphics, grDevices,
stringr, uuid, RCurl, base64enc, shiny, wrassp (>= 0.1.4),
jsonlite (>= 0.9.13), RSQLite (>= 1.0.0), DBI (>= 0.3.1),
httpuv (>= 1.3.2), dplyr (>= 0.4.3), readr (>= 1.1.1), tibble
(>= 1.4.2), purrr (>= 0.2.4), compare(>= 0.2.4)

MASS, tools, utils, stats, methods, graphics, grDevices,
stringr, uuid, RCurl, base64enc, shiny, wrassp (>= 0.1.4),
jsonlite (>= 0.9.13), RSQLite (>= 1.0.0), DBI (>= 0.3.1),
httpuv (>= 1.3.2), dplyr (>= 0.4.3), readr (>= 1.1.1), tibble
(>= 1.4.2), purrr (>= 0.2.4), compare(>= 0.2.4), rlang

MASS, tools, utils, stats, methods, graphics, grDevices,
stringr, uuid, RCurl, base64enc, shiny, wrassp (>= 0.1.4),
jsonlite (>= 0.9.13), RSQLite (>= 1.0.0), DBI (>= 0.3.1),
httpuv (>= 1.3.2), dplyr (>= 0.7.8), readr (>= 1.1.1), tibble
(>= 1.4.2), purrr (>= 0.2.4), compare(>= 0.2.4), tidyr(>=
0.8.2), git2r(>= 0.23.0), servr(>= 0.11), rlang

MASS, tools, utils, stats, methods, graphics, grDevices,
stringr, uuid, RCurl, base64enc, shiny, wrassp (>= 0.1.4),
jsonlite (>= 0.9.13), RSQLite (>= 1.0.0), DBI (>= 0.3.1),
httpuv (>= 1.3.2), dplyr (>= 0.7.8), readr (>= 1.1.1), tibble
(>= 1.4.2), purrr (>= 0.2.4), compare(>= 0.2.4), tidyr(>=
0.8.2), git2r(>= 0.23.0), mime(>= 0.6), rstudioapi(>= 0.10),
rlang

An R package that provides the EMU Speech
Database Management System (EMU-SDMS) with database management, data
extraction, data preparation and data visualization facilities.

An R package that provides the EMU Speech
Database Management System (EMU-SDMS) with database management, data
extraction, data preparation and data visualization facilities. See
<https://ips-lmu.github.io/The-EMU-SDMS-Manual/> for more details.

This package allows one to estimate the output of a
computer program, as a function of the input parameters,
without actually running it. The computer program is assumed to
be a Gaussian process, whose parameters are estimated using
Bayesian techniqes that give a PDF of expected program output.
This PDF is conditional on a ``training set'' of runs, each
consisting of a point in parameter space and the model output
at that point.  The emphasis is on complex codes that take
weeks or months to run, and that have a large number of
undetermined input parameters; many climate prediction models
fall into this class.  The emulator essentially determines
Bayesian a-postiori estimates of the PDF of the output of a
model, conditioned on results from previous runs and a
user-specified prior linear model.  A working example is given
in the help page for function `interpolant', which should be
the users's first point of reference.