Calibration of every computational code. It uses a Bayesian framework to rule the estimation. With a new data set, the prediction will create a prevision set taking into account the new calibrated parameters. The choices between several models is also available. The methods are described in the paper Carmassi et al. (2018) <arXiv:1801.01810>.
Version: | 0.1.1 |
Imports: | R6, ggplot2, DiceKriging, DiceDesign, MASS, coda, parallel, gridExtra, gtools |
LinkingTo: | Rcpp, RcppArmadillo, Matrix |
Suggests: | knitr, rmarkdown |
Published: | 2018-07-24 |
Author: | Mathieu Carmassi [aut, cre] |
Maintainer: | Mathieu Carmassi <mathieu.carmassi at gmail.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
CRAN checks: | CaliCo results |
Reference manual: | CaliCo.pdf |
Vignettes: |
Introduction to CaliCo |
Package source: | CaliCo_0.1.1.tar.gz |
Windows binaries: | r-devel: CaliCo_0.1.1.zip, r-release: CaliCo_0.1.1.zip, r-oldrel: CaliCo_0.1.1.zip |
OS X binaries: | r-release: CaliCo_0.1.1.tgz, r-oldrel: CaliCo_0.1.1.tgz |
Old sources: | CaliCo archive |
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