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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